Podcasts about Firebase

In this episode of Search Off the Record, Martin Splitt and John Mueller from Google's Search Relations team dive deep into the world of AI-assisted development. They explore the reality of "Vibe Coding", the process of building apps and websites using natural language instead of manual syntax. Whether you're a developer looking to offload tedious setup tasks or an SEO expert trying to understand how AI-generated sites impact search, this conversation is for you. In this episode, you'll learn: * What is Vibe Coding? Understanding the shift from writing syntax to "talking" to your IDE. * The Developer's Trap: Why you still need technical knowledge (like linters, deployment scripts, and GitHub Actions) to prevent AI from breaking your project. * SEO & AI Architecture: Why you can't just "add SEO" at the end—and how to guide AI to build with canonicals and sitemaps from day one. * Tooling Breakdown: Martin and John share their experiences with AI Studio, Gemini CLI, Firebase, and GitHub. * Testing with AI Agents: How to use AI to remote control browsers (like Chromium) for automated testing. Chapters 00:00 – Intro: What exactly is "Vibe Coding"? 01:32 – Martin's experiment with AI Studio and client-side JS. 03:30 – The "English as a Programming Language" allure. 06:00 – Why the AI makes assumptions (and why that's dangerous). 08:51 – "Sprinkling SEO" vs. Building for SEO from the start. 12:40 – Can AI test itself? Using browser agents for QA. 20:27 – The technical debt of AI: Refactoring and maintainability. 25:42 – Moving to the terminal: Gemini CLI & Cloud Code. 31:34 – Using AI to skip the setup work. Resources Mentioned: * Google AI Studio * Firebase Hosting * Gemini CLI / Cloud Code * GitHub Actions for CI/CD What's your experience with Vibe Coding? Let us know in the comments! Episode transcript → https://goo.gle/sotr110-transcript  Listen to more Search Off the Record → https://goo.gle/sotr-yt Subscribe to Google Search Channel → https://goo.gle/SearchCentral  Search Off the Record is a podcast series that takes you behind the scenes of Google Search with the Search Relations team.  #SOTRpodcast #SEO #GoogleSearch Speakers: Martin Splitt, John Mueller

If you've ever said “We'll just set up a walking blood bank when we need it,” this episode will make you rethink everything. Dennis and Andrew Fisher drop straight fire on how to actually build, stock, train, and run a real walking blood bank on a FOB, Firebase, or any austere base — not just theory, but the exact steps special operators and conventional medics are using right now to save lives when the next mass casualty hits.No fluff. No “somebody else will handle it.” Just battle-tested, practical guidance on turning your team (and the units around you) into a living blood bank that can deliver fresh whole blood in under 30 minutes.Key Takeaways You Can Use TomorrowPre-type every donor (especially O's) and keep the roster with key leaders and medics — Medpros + secondary confirmation beats dog tags every time.Distribute kits across the team so one casualty doesn't wipe out all your supplies.Practice full collections with non-medics — they can (and will) be your force multipliers.Have donor questionnaires filled out in advance for anyone outside your unit; do Eldon cards in calm conditions, never under fire.Plan for 20–30 minutes from alert to transfusion — that window dictates how long you have to bridge with other resuscitation tools.Principles over perfection: good stick + patent line + practiced team beats fancy equipment every single time.Chapters00:00 – Welcome & Why Most Walking Blood Banks Stay TheoreticalThe dangerous gap between “we have a plan” and actually practicing it.02:30 – Preferred Blood & ABO Typing Your Entire ForceLow-titer O whole blood, Medpros screening, lab vs. Eldon cards, and why you double-type.08:45 – Eldon Cards: When They Work (and When They Don't)Calm pre-mission testing vs. chaos — real talk on reliability.13:20 – Supplies & Logistics: Bags, Kits, Refrigeration & Cold ChainFenwal vs. Terumo, how many kits to order, and smart storage hacks.19:10 – Point-of-Injury Kits & Load DistributionWhat medics carry, what teammates carry under plates, and spreading risk.24:40 – IV Technique, Saline Locks & Point-of-Care TestingWhy 18-gauge + PRN adapter wins, donor screening, and host-nation considerations.31:15 – Donor Questionnaires & Pre-ScreeningWhen to use them, multilingual options, and why you do this before the fight.35:50 – Selling It to Commanders & Multi-Unit CoordinationRisk-benefit talk that actually works: mutual support, 100+ years of history, and 10,000+ units transfused.41:20 – Real Timelines: 20–30 Minutes from Call to TransfusionTraining goals, the 15-minute bag-fill rule, and why practice beats classroom speed.47:30 – Closing Principles & Final ThoughtsForce multiplication, non-medics stepping up, and adapting under pressure.Whether you're ODA, Ranger, conventional, or just preparing for the next deployment — this is the episode that turns “we should do a walking blood bank” into “here's exactly how we're doing it.”For more content, go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.prolongedfieldcare.org⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Consider supporting us: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠patreon.com/ProlongedFieldCareCollective⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ or ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.lobocoffeeco.com/product-page/prolonged-field-care⁠

This is a recap of the top 10 posts on Hacker News on April 16, 2026. This podcast was generated by wondercraft.ai (00:30): Claude Opus 4.7Original post: https://news.ycombinator.com/item?id=47793411&utm_source=wondercraft_ai(01:58): Qwen3.6-35B-A3B: Agentic coding power, now open to allOriginal post: https://news.ycombinator.com/item?id=47792764&utm_source=wondercraft_ai(03:26): Codex for almost everythingOriginal post: https://news.ycombinator.com/item?id=47796469&utm_source=wondercraft_ai(04:54): The local LLM ecosystem doesn't need OllamaOriginal post: https://news.ycombinator.com/item?id=47788385&utm_source=wondercraft_ai(06:22): The future of everything is lies, I guess: Where do we go from here?Original post: https://news.ycombinator.com/item?id=47792718&utm_source=wondercraft_ai(07:50): Darkbloom – Private inference on idle MacsOriginal post: https://news.ycombinator.com/item?id=47788542&utm_source=wondercraft_ai(09:18): Cloudflare Email ServiceOriginal post: https://news.ycombinator.com/item?id=47792593&utm_source=wondercraft_ai(10:46): €54k spike in 13h from unrestricted Firebase browser key accessing Gemini APIsOriginal post: https://news.ycombinator.com/item?id=47791871&utm_source=wondercraft_ai(12:14): FSF trying to contact Google about spammer sending 10k+ mails from Gmail accountOriginal post: https://news.ycombinator.com/item?id=47788424&utm_source=wondercraft_ai(13:42): Qwen3.6-35B-A3B on my laptop drew me a better pelican than Claude Opus 4.7Original post: https://news.ycombinator.com/item?id=47796830&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

https://itayverchik.co.il/ai-kills-saas/הבינה המלאכותית משנה את חוקי המשחק ופוגעת אנושות במודל ה-SaaS (תוכנה כשירות) המסורתי. למה לשלם עשרות דולרים בחודש לפלטפורמות חיצוניות עבור מעקב אחרי תעודות הסל והמניות שלכם, כשאפשר לבנות מערכת חכמה, מדויקת ומותאמת אישית לגמרי לבד?בסרטון הזה אני חושף איך מפתחים מערכת SaaS שלמה מאפס. אנחנו הולכים לבנות סורק מניות ומערכת לניתוח תיקי השקעות, שמבוססת על המנוע העוצמתי של Google AI Studio, עם ניהול משתמשים מלא דרך Firebase. בנוסף, נראה איך להפוך את המערכת הזו לעסק של ממש על ידי חיבור למערכת סליקה.מה נראה במדריך?ארכיטקטורת המערכת: איך מחברים בין המוח של הבינה המלאכותית (AI Studio) לבין צד הלקוח והשרת.ניהול נתונים ב-Firebase: הקמת מסד נתונים (Database) לשמירת נתוני המשתמשים, היסטוריית החיפושים והרכב תיק ההשקעות שלהם בצורה מאובטחת.ניתוח שוק ההון ב-AI: בניית הפרומפטים והלוגיקה שיאפשרו למערכת לסרוק מניות, לנתח מגמות ולספק תובנות על הרכב התיק.מונטיזציה וסליקה: איך לחבר פתרון סליקה (Payment Gateway) כדי לגבות תשלום ממשתמשים על גישה למערכת ה-AI שפיתחתם.המדריך הזה ייקח אתכם משלב הרעיון ועד למוצר טכנולוגי עובד שיכול לייצר לכם הכנסה.המדריך פתח לכם רעיונות לסטארטאפ הבא שלכם?אל תשכחו לעשות לייק לסרטון, להירשם לערוץ וללחוץ על הפעמון כדי לקבל עדכונים על עוד פיתוחי בינה מלאכותית, אוטומציות ומערכות מורכבות.

Wat als je straks nooit meer een website bezoekt, maar je AI alles voor je regelt — inclusief hotels boeken, apps kiezen en beslissen welke bedrijven jouw aandacht verdienen? Robert-Jan Huijsman is founding engineer bij Reboot en voor de zoveelste keer te gast bij MNOT. Dit keer legt hij uit wat MCP-apps zijn: een gloednieuwe manier waarop bedrijven visuele, interactieve apps kunnen afleveren bínnen je AI-chatvenster. Denk aan een Booking.com-interface die opduikt terwijl je met Claude praat. Robert-Jan schetst hoe AI de derde voordeur van het internet wordt — naast websites en mobiele apps — en waarom dat enorme kansen biedt, maar ook bekende gevaren. Want wie bepaalt straks welke app jouw AI aanbeveelt? Randal en Jurian trekken parallellen met de opkomst van Google als gatekeeper en vragen zich af of Tweakers en andere mediabedrijven deze golf gaan overleven. Tussendoor duiken we de technische kant in: waarom is het MCP-protocol eigenlijk zo eigenwijs gebouwd, en hoe lost Reboot precies dat probleem op? Over Robert-Jan Huijsman Robert-Jan Huijsman is founding engineer bij Reboot (reboot.dev), een startup die een framework bouwt voor durable backends en MCP-apps. Eerder was hij medeoprichter van Tracis (een lens-analytics-startup) en senior software engineer bij Google, waar hij aan Firebase werkte. Hij woont in Kopenhagen en is een van de meest terugkerende gasten bij MNOT. LinkedIn: https://www.linkedin.com/in/rjhuijsman/ GitHub: https://github.com/rjhuijsman Reboot: https://reboot.dev Tijdschema 0:00:00 Robert-Jan is terug — voor de tiende(?) keer0:02:10 MNOT bestaat negen jaar (en niemand had het door)0:04:40 Van servers ophangen naar vibe coding en MCP ontdekken0:05:13 MCP uitgelegd: hoe je AI gereedschap leert gebruiken0:10:54 MCP-apps: visuele interfaces ín je AI-chat0:18:10 “Je AI wordt een soort operating system”0:27:11 Gatekeepers 2.0: wordt Claude de nieuwe Google?0:28:42 Hoe Tweakers en DPG Media omgaan met de AI-golf0:35:34 Bias, advertenties en het manipuleren van AI-antwoorden0:44:40 Hoe Reboot per ongeluk perfect gepositioneerd bleek0:51:13 Waarom het MCP-protocol eigenlijk best raar in elkaar zit0:55:03 De gemeente-balie-analogie: stateful vs. stateless uitgelegd1:10:57 Tailscale als voorbeeld: hoe AI jouw toolkeuzes bepaalt Genoemd in deze aflevering Reboot (reboot.dev) — het framework van Robert-Jan voor durable backends en MCP-apps MCP (Model Context Protocol) — het protocol waarmee AI-tools en apps aanstuurt MCP-apps — de nieuwe uitbreiding waarmee visuele interfaces in AI-chats verschijnen Claude / Claude Code — AI-assistent en coding tool van Anthropic ChatGPT / OpenAI — concurrent AI-platform Firebase — Google’s backend-as-a-service (waar Robert-Jan eerder aan werkte) Tailscale — VPN-tool die door AI-assistenten actief wordt aangeraden Tweakers / DPG Media — als voorbeeld van mediapartijen vs. AI-gatekeepers Goose — populaire open-source AI-client Tips van de tafel Randal: Draai een eigen AI-model lokaal op een Mac Mini of server met videokaart. De lokale modellen zijn misschien een halfjaar achter op de frontier-modellen, maar voor veel taken prima. En je bent niet afhankelijk van gatekeepers.Robert-Jan: Heb je vragen over MCP of Reboot? Spring op de MNOT Slack of op de Reboot Discord — daar is Robert-Jan dagelijks actief en het is letterlijk zijn baan om te antwoorden.See omnystudio.com/listener for privacy information.

Google AI Studio just got databases, multiplayer, and persistent sessions. Vibecoding just grew up. We break down what it means for your AI workflow. This week on AI For Humans, we dig into Google's massive AI Studio upgrade that levels up vibecoding with Firebase databases, authentication, multiplayer support, persistent sessions, and Next.js integration. We discuss how it compares to Claude Code and Codex, and ask whether this changes the vibecoding stack for good.  Plus Google Stitch for design-to-code, OpenAI cuts side projects to focus on coding and fend off Anthropic, Claude Code Dispatch lets you run Cowork from your phone, DLSS 5 drama continues, Midjourney v8 launches to mixed reviews, Runway teams up with NVIDIA's Vera Rubin chip for near real-time video generation, Val Kilmer gets resurrected by AI to star in a new film, Meta's having trouble with rogue AI agents, Kagi's LinkedIn translator is hilarious. Oh and Kevin created open-source Generative DOOM. GOOGLE GOT A NEW TOOLBOX. WE OPENED IT. THINGS GOT WEIRD. #ai #google #ainews Come to our Discord: https://discord.gg/muD2TYgC8f Join our Patreon: https://www.patreon.com/AIForHumansShow AI For Humans Newsletter: https://aiforhumans.beehiiv.com/ Follow us for more on X @AIForHumansShow Join our TikTok @aiforhumansshow To book us for speaking, please visit our website: https://www.aiforhumans.show/ // Show Links // Google AI Studio https://aistudio.google.com/ Google AI Studio New Features Demo Videos https://x.com/GoogleAIStudio/status/2034654985850659149 Google Stitch: Design to Code with AI https://stitch.withgoogle.com/docs/design-md/overview Google Stitch Launch Announcement https://x.com/stitchbygoogle/status/2034332847893574080?s=20 OpenAI Cuts Back on Side Projects to Focus on Coding and Fend Off Anthropic https://futurism.com/artificial-intelligence/openai-cutting-projects OpenAI Cuts Back on Side Projects (WSJ) https://www.wsj.com/tech/ai/openai-chatgpt-side-projects-16b3a825 Claude Code Dispatch: Run Claude Cowork and Code From Your Phone https://x.com/felixrieseberg/status/2034381385134399913?s=20 How Claude's Team Uses Skills Internally https://x.com/trq212/status/2033949937936085378 Claude Superpowers Skill https://github.com/obra/superpowers DLSS 5 Drama: Digital Foundry Follow-Up https://youtu.be/5dTTfjBAFzc?si=gqiBAyahasQxFA0U DLSS Anything: Open Source AI Upscaling on Hugging Face https://huggingface.co/spaces/victor/dlss-5-anything Midjourney v8 Launch Announcement https://x.com/midjourney/status/2034015403542974793?s=20 Runway + NVIDIA Vera Rubin Chip: Near Real-Time Video Generation https://x.com/runwayml/status/2034284298769985914?s=20 Val Kilmer Resurrected With AI to Star in New Film https://variety.com/2026/film/news/val-kilmer-ai-film-as-deep-as-the-grave-1236691042/ Meta Is Having Trouble With Rogue AI Agents https://techcrunch.com/2026/03/18/meta-is-having-trouble-with-rogue-ai-agents/ Kagi's LinkedIn Speak Translator https://translate.kagi.com/ Kagi LinkedIn Translator (Fast Company Coverage) https://www.fastcompany.com/91511316/this-eerily-accurate-linkedin-speak-translation-tool-will-help-you-sound-like-an-instant-thinkfluencer Generative DOOM: Open Source AI-Powered DOOM (AI For Humans Project) https://github.com/0xunderl0rd/AI4H-DoomGen  

Howdy, Alex here, let me catch you up on everything that happened in AI: (btw; If you haven't heard from me last week, it was a Substack glitch, it was a great episode with 3 interviews, our 3rd birthday, I highly recommend checking it out here) This week was started on a relatively “chill” note, if you consider Anthropic enabling 1M context window chill. And then escalated from there. We covered the new GPT 5.4 Mini & Nano variants from OpenAI. How MiniMax used autoresearch loops to improve MiniMax 2.7, Cursor shipping their own updated Composer 2 model, and how NVIDIA CEO Jensen Huang embraced OpenClaw calling it “the most important OSS software in history” and that every company needs an OpenClaw strategy. Also, OpenAI acquires Astral (ruff, uv tools) and Mistral releases a “small” 119B unified model and Cursor dropped their Opus like Composer 2 model. Let's dive in: ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.Big Companies LLMs 1M context is now default for Opus.Anthropic enabled the 1M context window they shipped Claude with in beta, by default, to everyone. Claude, Claude Code, hell, even inside OpenClaw if you're able to get your Max account in there, are now using the 1M long version of Opus. This is huge, because, while its not perfect it's absolutely great to have 1 long conversation and not worry about auto-compaction of your context. As we just celebrated our 3rd anniversary, I remember that back then, we were excited to see GPT-5 with 8K context. Love how fast we're moving on this. OpenAI drops GPT-5.4 mini and nano, optimized for coding, computer use, and subagents at a fraction of flagship costLast week on the show, Ryan said he burned through 1B (that's 1 billion) tokens in a day! That is crazy, and there's no way a person sitting in front of a chatbot can burn through this many tokens. This is only achieved via orchestration. To support this use-case, OpenAI dropped 2 new smaller models, cheaper and faster to run. GPT 5.4 Mini achieves a remarkable 72.1% on OSWorld Verified, which means it uses the computer very well, can browse and do tasks. 2x faster than the previous mini, at .75c/1M token, this is the model you want to use in many of your subagents that don't require deep engineering. This is OpenAI's ... sonnet equivalent, at 3x the speed and 70% the cost from the flagship. Nano is even crazier, 20 cents per 1M tokens, but it's not as performant, so I wouldn't use it for code. But for small tasks, absolutely. Here's the thing that matters, these models are MEANT to be used with the new “subagents” feature that was also launched this week in Codex, all you need to do as... ask! Just tell Codex “spin up a subagent to do... X” and it'll do it.OpenAI shifts focus on AI for engineering and enterprise, acquires Astral.sh makers of UV. Look, there's no doubt that OpenAI the absolutely leader in AI, brought us ChatGPT, with over 900M users using it weekly. But they see what every enterprise sees, developers are MUCH more productive (and slowly so are everyone else) when they use tools that can code. According to WSJ, OpenAI executives will reprioritize some of the side-quests they have (Sora?) to focus on productivity and business. Which essentially means, more Codex, more Codex native, more productivity tools.With that focus, today they announced that OpenAI / Codex is acquiring Astral, the folks behind the widely popular UV python package manager. This brings strong developer tools firepower to the Codex team, the astral folks are great at writing incredibly fast tools in rust! Looking forward to see how these great folks improve Codex even more. Jensen Declares Total OpenClaw Victory at GTC, Announces NemoClaw (Github)This was kind of surreal, NVIDIA CEO Jensen Huang, is famous for doing his stadium size keynote, without a teleprompter, and for the last 10 minutes or so, he went all in on OpenClaw. Calling it “the most important OSS software in history” and outlining how this is the new computer. That Peter Steinberger with OpenClaw showed the world a blueprint for the new coputer, an personal agentic system, with IO, files, computer use, memory, powered by LLMs. Jensen did outline that the 3 things that make OpenClaw great are also the things that enterprises cannot allow, write access to your files + ability to communicate externally is a bad combo, so they have launched NemoClaw.They've got a bunch of security researchers to work with OpenClaw team to integrate their new OpenShell sandboxing effort, network guardrails and policy engine integration. I reminded folks on the pod that the internet was very insecure, there was a time where folks were afraid of using their creditcards online. OpenClaw seems to be speed running that “unsecure but super useful” to “secure because it's super useful” arc and it's great to see a company as huge as NVIDIA embrace. Not to mention that given that agents can run 24/7, this means way more inference and way more chips sold for NVIDIA so makes sense for them, but still great to see!Manus “my computer” and other companies replicating “OpenClaw” successThis week it became clear, after last weeks Perplexity “computer”, Manus (now part of Meta) has also announced a local extension of their cloud agents, and those two are only the first announcements, it's clear now that every company dissected OpenClaw's moment and will be trying to give its users what they want. An agentic always on AI assistant with access to the users files, documents etc. Claude code added “channels“ support with telegram and discord connectors today, which, also, is one big missing piece of the puzzle for them. Everything is converging on this. Even OpenAI is rumored to consolidate Codex (which sees huge success) with OpenAI and Atlast browser into 1 “mega” APP that would do these things and act as an agent. ThursdAI - Highest signal weekly AI news show is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.MiniMax M2.7: The Model That Built Itself This one blew me away, it's not quite open source (yet?) but the MiniMax folks are coming out with a 2.7 version just after their MiniMax 2.5 was featured on our show and .. they are claiming that this model trained itself. Similarly to Andrej Karpathy's auto-researcher, the MiniMax folks ran 100+ autonomous optimization loops, t get this model to 56.22% on the hard Swe-bench pro benchmark (close to Opus's 57.3%!) and this one gets a 88% win rate vs the very excellent MiniMax 2.5. They used the previous model to build the agent harness and scaffolding, with 1 engineer babysitting these agent, and writing 0 lines of human code, which as we said before, every company will be doing, as we're staring singularity in the face! We've evaluated this model as well (Wolfram has been busy this week!) and it's doing really well on WolfBench with 52% average and 64% top score, it's very close to 5.3 codex on our terminalBench benchmark! We hope that this model will be open source at some point soon as well! Cursor drops Composer 2 - nearly matching Opus 4.6, fast version (Blog)Cursor decided to add to our show's breaking news record of Thursday releases with a brand new in-house trained Composer 2. This time they released more benchmarks than only their internal “composer bench” and this model looks great! (we are pretty sure it's a finetune of a chinese OSS model, but we don't know which) Getting 61% on Terminal Bench, beating Opus 4.6 is quite a significant achievement, but coupled with the incredible pricing they are offering, $0.5/1Mtok input and $2.50/M output tokens, Cursor is really aiming for the productivity folks and showing that they are more than just an IDE.Early users are reporting noticeably cleaner code than both Opus and Composer 1.5 — better adherence to clean code principles, smarter multi-file implementations, and strong performance on long-horizon agentic tasks like full API migrations and legacy codebase refactoring. They also shipped a new interface called Glass (in alpha) that's built for monitoring these long-running agent loops. Open Source: Mistral is Back, BabyMistral Small 4: 119B MoE with 128 experts + Apache 2.0 (X, Blog, HF)It's been a while since Mistral dropped something properly open source, and this week they kicked off what looks like their fourth generation with Mistral Small 4. The name is a little funny given the actual size — 119 billion total parameters, 128 experts in the mixture — but with only 6 billion active per token. So you get the knowledge footprint of a massive model but the compute profile of a small one. Very MoE-brained.The bigger story here is what's unified inside: this is Magistral (reasoning), Pixtral (multimodal), and Devstral (coding) all rolled into one weights file. Previously you had to choose which Mistral “side quest” model you wanted. Now there's a reasoning_effort parameter where you dial from none for fast cheap responses all the way up to high for step-by-step thinking, no model switch required. How does it perform? We ran it through WolfBench and it landed toward the lower end of Wolfram's current leaderboard — around 17% on the agentic tasks, roughly on par with Nemotron at the same scale. It's not competing with Opus or GPT-5.4, and we weren't really expecting it to. What we're excited about is that it does multimodal, reasoning, and coding in one Apache-licensed package, and people are already running IQ4 quants locally. Shout out to Mistral for the return to open source — it's been a minute, and the community noticed.Unsloth Studio: Fine-Tuning Gets a UI (Blog)Something I think people are sleeping on this week is Unsloth Studio, the open-source web UI that the Unsloth team just launched for local LLM training and inference. Unsloth has been quantizing and compressing models better than basically anyone for a while now — 2x training speed, 70% less VRAM, zero accuracy loss — but that was all code-first. Studio is the no-code interface layer on top of all of that.The numbers: supports 500+ models across text, vision, audio, and embeddings. It runs 100% offline with no telemetry. Julien Chaumond, the CTO of Hugging Face, confirmed it trains successfully on a Colab Pro A100. There's even a free Colab notebook for models up to 22B parameters. For folks who want to fine-tune models overnight without spinning up cloud infra or wrestling with Docker, this is a genuine leap forward. Nisten compared it to what LM Studio did for local inference — making something that used to require deep expertise suddenly accessible to anyone. I think that comparison is spot on, and I want to get Daniel and the Unsloth team on the show to dig into this properly.This Week's Buzz: W&B iOS App & The Overthinking ParadoxThe iOS App is Finally Here (app store)Okay, I'm going to do a quick applause.

>Join Jocko Underground< Abandoned in Hell: The Fight for Vietnam's Firebase Kate: When Firebase Kate was surrounded by a determined enemy force, there were no guarantees of relief and no easy way out. Cut off, outnumbered, and under constant attack, the men on the hill had one mission: hold the line and survive. This is the story of fear, faith, leadership under fire, and the breakout that got them off the hill alive.Support this podcast at — https://redcircle.com/jocko-podcast/exclusive-content

Tekoälykoodaus huipputasolla | Markus Hav | 371 Neuvottelija. Markus Hav kuvaa tekoälykoodauksen terävintä kärkeä ja intensiteettiä. Tekoälyagentit muokkaavat ajattelua, työtä ja oppimista kiihtyvällä vauhdilla. Hav korostaa kontekstia, muistia ja intentioita sekä "codumentantaatiota" koodauksessa.2026 on tekoälykoodauksen käännekohta varsinkin Anthropicin ja Googlen vyöryttäessä huipputyökaluja.00:00 Anthropicin Claude Code pyyhkii tekoälypöydän00:19 Markdown pyörittää AI:ta mutta missä on hyvä editori?00:30 Claude Code Max tokenilompakot Markus €400/kk vs Sami €100/kk00:57 Lex Friedmanin tylsä tekoälyjakso?01:45 Me ei olla hei hulluja, vaan maailma syöksyy tekoälyyn02:06 Tekoälyn eksponentiaalinen kehitys 2026 vs seesteinen 202502:34 Claude Coden tokenit tekevät paljon todellista työtä03:24 Ennuste Googlen ja Geminin noususta ja miksi se oli loogista jo vuosi sitten04:09 Googlen data, piirit ja mallit yhdistyvät ylivoimaiseksi kokonaisuudeksi05:04 Asennetaan Open Claw Samin Mac Miniin!05:42 Ihmisriskin merkitys ja miksi first mover ei aina voita07:01 Tietoturvan meni uusiksi07:21 Samantha-bot auttaa Samia ja palkitaan HoxHunt koululla09:08 Ovatko agentit lojaaleja tai reagoivat kannustimiin?10:44 Agentti ei ole enää sama keskustelusta toiseen11:26 Kontekstiikkuna muistina ja sen tyhjentäminen11:57 Miljoonan tokenin aikakausi ja sen rajat13:51 Second Brain ja rajattoman kontekstin haave14:19 Muistihaku tekee agentista ihmistä tehokkaamman15:00 Ralph Wiggum malli eli tyhmä mutta sinnikäs agentti15:45 Orkestroinnin sijaan yksinkertainen tehtävälista17:31 Valmiit skill md:t ja työkalujen kypsyminen18:12 Kaikkea ei ehdi kokeilla ja se on ok20:19 Uudet työntekijät saavat AI työkaluista eniten irti21:34 Vasaraongelma ja vanhoihin teknologioihin jämähtäminen22:12 Myös kivikautiset järjestelmät kuten Cobol avautuvat tekoälylle23:30 Gemni vs Claude Code vs Codex vs xAI24:39 One shot koodaus vs oppiminen virheiden kautta26:12 Itsereflektio uutena mallikyvykkyytenä27:15 Agentti tunnistaa itsensä ja parantaa itseään28:07 Resilienssi virheisiin koodauksessa29:20 Käyttöliittymä ratkaisee oppimiskokemuksen30:12 Mallien erilaiset virheprofiilit31:24 Google Antigravity ja käytännön työnkulut32:17 Frontend backend ja tietokantarealismi33:15 Demoefekti ja turhautuminen uusissa työkaluissa34:26 Firestore vs Supabase35:26 Lovable apupyörinä ja nopean alun mahdollistajana36:25 Dokumentaation ja speksin ero37:48 Hyvä speksi on jo puoliksi tehty työ39:19 Intentioiden rajaaminen agenttien hallintaan40:05 Ajatus intentiotietokannasta GitHubin sijaan41:24 Ymmärrä miksi rakennat et vain miten42:13 Markdown ja Obsidian Second Brainin perustana43:11 Microsoftin hapuilu ja Anthropicin MS Excel PowerPoint CoWork44:16 Anthropic kaikissa pilvissä strategisena voittajana45:31 OpenAI ei kuole mutta suunta hämärtyy46:00 Elon Musk SpaceX ja xAI:n inferenssi avaruudessa50:35 Neurolink ja suora yhteys tekoälyyn52:40 Epämiellyttävät ihmiset joilla on valtava vaikutus54:41 Kiinalaiset mallit ja agenttinen rynnäkkö56:39 Satojen miljoonien tokenien orkestrointi57:36 Halvat mallit mahdollistavat massiiviset agenttijoukot58:31 2026 käännekohtana ihmiskunnan historiassa59:12 Käytännön ensiaskel agenttiseen tekemiseen1:00:04 Agenttiset selaimet ja varovaisuuden tarve1:01:23 Lovable ja Firebase matalan kynnyksen alkuun1:02:13 Oivallus että kuka tahansa voi rakentaa1:03:35 Yhdessä tekeminen oppimisen katalyyttinä1:04:07 Käyttöliittymien kuolema - UX is dead?#neuvottelija Sisäpiirissä kekustellaan onko käyttöliittymä ihmisille turhaa koodia,Katso Sisäpirijaksot ja tue Samia#neuvottelija Sami Miettinen

Before you soft launch, check this. Or your data will lie. In this episode, Matej walks through a complete soft launch checklist covering tracking, attribution, monetization hygiene, pricing, live ops readiness, UA setup, and creatives.This is not a full guide. The full guide is here: https://payhip.com/b/fJi9EIt's everything that must work before you judge CPI, retention, or ROAS.What Matej covers• MMP & Firebase setup• iOS ATT and conversion schemas• Crash & ANR thresholds• Revenue deduplication & fraud• Ad mediation basics• Geo pricing strategy• Content & live ops readiness• Store listings & creativesKey takeaway:If the setup is broken, nothing matters. ---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Podcast: Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-2um8eguhf-c~H9idcxM271mnPzdWbipgChapters00:00 — Why most soft launches fail01:15 — The technical phase nobody skips (but should)02:40 — MMP, Firebase, and key events04:20 — iOS conversion schemas & ATT logic05:50 — Crashes, ANRs, and store penalties07:10 — Revenue deduplication mistakes08:20 — Purchase validation & cheaters09:30 — Ad mediation basics in soft launch11:00 — Balancing ads vs retention12:10 — Geo pricing and monetization power13:30 — Content roadmap & live ops thinking14:50 — UA readiness & campaign setup16:00 — Store listings, ASO & keywords17:20 — Creatives for soft launch (what NOT to fake)---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me---------------------------------------If you are interested in getting UA tips every week on Monday, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠lancaric.substack.com⁠⁠⁠⁠⁠⁠ & sign up for the Brutally Honest newsletter by Matej LancaricDo you have UA questions nobody can answer? Ask ⁠⁠⁠⁠⁠⁠⁠⁠Matej AI⁠⁠⁠⁠⁠⁠ - the First UA AI in the gaming industry! https://lancaric.me/matej-ai

In this solo episode, Matej breaks down his MVP Google Ads UA setup for mobile games and apps in 2026. From Firebase tracking and conversion windows to campaign structure, audience signals, creatives, and optimization cadence - this is a practical, field-tested Google Ads playbook.More info here: https://lancaric.substack.com/p/mvp-ua-template-i-want-to-launch-f9eWhat you'll learn• Why Firebase is mandatory for Google UA• Which events to bid on (and which to avoid)• How conversion windows affect learning speed• Why fewer campaigns scale better• How to use audience signals properly• When to move into ROAS campaigns• How to structure creatives & ad groups• Why patience beats constant optimizationGet our MERCH NOW: 25gamers.com/shopThis is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-2um8eguhf-c~H9idcxM271mnPzdWbipg00:00 — Why Google Ads still matters in 202601:50 — Tracking fundamentals: Firebase vs MMP04:30 — What events to bid on (installs vs actions)07:00 — Conversion windows: speed vs accuracy09:40 — Campaign structure & geo tiers12:30 — Audience signals that actually work15:10 — When (and when not) to run ROAS campaigns17:30 — Creative structure & ad groups18:50 — The “hamburger” optimization strategy19:40 — Final advice & common Google Ads mistakes---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me---------------------------------------If you are interested in getting UA tips every week on Monday, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠lancaric.substack.com⁠⁠⁠⁠⁠⁠ & sign up for the Brutally Honest newsletter by Matej LancaricDo you have UA questions nobody can answer? Ask ⁠⁠⁠⁠⁠⁠⁠⁠Matej AI⁠⁠⁠⁠⁠⁠ - the First UA AI in the gaming industry! https://lancaric.me/matej-ai

Die Balance zwischen Familie, Konzernjob und Side Project.Side Project, Vollzeitjob und dann auch noch Kinder. Klingt nach einer dieser Ideen, die man sonntags feiert und montags bereut. Aber was, wenn genau darin die Energie steckt, die dir im Konzernalltag fehlt? Und was, wenn die größte Challenge gar nicht Zeit ist, sondern Erwartungen, Selbstzweifel und der Druck, immer liefern zu müssen?In dieser Episode sprechen wir mit Stephan, iOS-Software-Engineer bei der Techniker Krankenkasse, Quereinsteiger mit McKinsey-Background, Vater von zwei Kindern und Indie-Developer der Haushaltsbuch-App Monee. Stephan nimmt uns mit in seine Hypercare-Phase als Elternteil, erklärt sein Setup mit Vier-Tage-Woche, Kinderbetreuung und klaren Absprachen und zeigt, wie er ein Side Project so baut, dass es nicht die Familie frisst.Wir gehen tief in Energiemanagement, Autonomie als Motivator, Support-Triage, den Umgang mit Crashs und negativen Reviews sowie in die Realität von Build-in-Public, inklusive Survivorship Bias. Dazu gibt es ehrliche Einblicke darin, wie man als Entwickler:in trotz wenig Zeit dranbleibt, ohne sich selbst zu zerlegen.Wenn du dich fragst, wie du Weiterbildung, Open Source oder ein eigenes Produkt neben Familie und Job realistisch unterkommst, ist das deine Episode.Bonus: Elternlogik des Tages. Ein Kind ist kein Kind. Du bist noch in der Überzahl.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

In this episode of The Digital Marketing Podcast, Daniel Rowles introduces us to the process of Vibe Coding, a revolutionary approach to software development that leverages AI to make app creation accessible to anyone, regardless of coding experience. Vibe coding shifts the focus from writing manual code to guiding AI with natural language prompts, allowing non-developers to build interactive apps, tools, and even businesses. Daniel explores the three tiers of vibe coding, from basic one-page web apps to full-scale, secure, AI-powered platforms , and shares practical steps, tools and security tips to get started. The second half of the episode features a compelling interview with Christo Snyman, a podcast listener who used vibe coding to launch his AI assistant platform Traderly.ai. Christo takes us behind the scenes of building a real-world startup with no prior coding background, sharing his full tech stack, hard-earned lessons, and the mindset needed to succeed. In This Episode: What is Vibe Coding? Understand how natural language prompts can now be used to create working code, dramatically lowering the barrier to digital creation. The Three Levels of Vibe Coding Level 1: Build one-page apps using HTML, CSS, and React, no backend required Level 2: Add memory, interactivity, and live AI responses through API access Level 3: Create fully-fledged apps with user authentication, databases, and deployment Practical Use Cases - From interactive Google algorithm timelines to embedded AI tools for keyword research and content planning, Daniel shares how these tools are being used on Target Internet's own website. Christo's Journey - From Idea to Startup. Learn how Christo turned a common business pain point — small service businesses missing leads due to message overload — into a scalable SaaS platform. Discover his full tech stack including React, Azure Functions, PostgreSQL, Firebase, OpenAI, WordPress, and more. Key Takeaways: Anyone can now build apps using AI tools, whether it's a timeline, calculator, chatbot, or full customer-facing product. AI-assisted development removes fear and unlocks creativity, especially for entrepreneurs without a dev background. Start small and iterate, your Minimal Viable Product doesn't need to be perfect, it needs to be useful. Vibe coding is the bridge between ideas and execution - fast, flexible and increasingly powerful. The future of marketing and tech creation is conversational and it's already here.

Discover Firebase Data Connect with Tyler Crowe! Dive into SQL, GraphQL, AI, and live demos to supercharge your app's data. Share your thoughts below!https://codingcat.dev/podcast/firebase-sql-with-data-connect00:00 Data Connect Overview04:29 AI Features & Vertex13:13 SQL Views & Raw SQL15:45 Downloading SDKs24:37 Full Text Search30:32 Vector Search Explained38:20 GraphQL Insights47:43 Firestore vs Data Connect48:32 Conclusion

In this solo episode, Matej walks through the exact step-by-step method he uses to launch successful Meta (Facebook) UA campaigns in 2025. From SDK setup to event engineering, targeting, budgets, creative strategy, and daily management - this is the “MVP strategy” every UA manager needs.Direct links to step-by-step guides:https://lancaric.substack.com/p/mvp-ua-template-i-want-to-launchhttps://lancaric.substack.com/p/mvp-ua-iaa-template-i-want-to-launch?triedRedirect=truehttps://lancaric.substack.com/p/creative-brief-template-7-creativeSoftlaunch E-BOOK: https://payhip.com/b/fJi9EWhat you'll learn• Minimum viable tracking setup (Facebook SDK, Firebase, MMPs)• How to build meaningful event combinations (WY S WYG)• Purchase-optimized vs. app-event optimized campaigns• How to structure T1, T2, and ROW for better learning• Targeting that still works in 2025 (broad, genre interest, lookalikes)• Budget pacing rules and when to scale• 3 starting concepts → 9 ad variations• Using AI hooks, memes, UGC & dev-cam videos• Daily operations: When you should do nothing• Moving from AEO to value optimization• Live example: Creating a full Meta campaignActionable checklistIntegrate Facebook SDK + Firebase + MMP.Build 5–10 event combinations (engagement + monetization).Start with 3 concepts → 9 variations.Use T1 (US/UK/DE/CA/AU/NZ/KR/JP) + ROW.Don't scale before day 3–5 stability.Check KPIs 2–3× per day, not hourly.Switch to value optimization once you have depth of payers.Key takeawayYour UA performance is defined by the events you send, the creatives you test early, and how fast you can validate your first 3 concepts.Get our MERCH NOW: 25gamers.com/shop---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Podcast: Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-2um8eguhf-c~H9idcxM271mnPzdWbipgChapters00:00 — Cold open & why this solo episode matters00:40 — The original problem: “Where do I start with Meta UA?”01:20 — Tracking essentials (SDKs, MMP, events you MUST send)03:00 — WYSIWYG: “What You Send Is What You Get” explained04:20 — Event combinations that actually help Facebook find payers05:40 — Purchase vs. App Event optimization: when to use each07:00 — Geo setup: US, Tier 1, ROW, and why you always need all three08:10 — Targeting in 2025: broad, interests, lookalikes (what still works)09:30 — Budgets: first 7-day rules & how not to break learning11:00 — Creative framework: 3 concepts → 9 ad variations12:30 — What concepts to start with (gameplay+finger, AI hooks, UGC/dev-cam)14:00 — Creative iterations: hooks, pacing, soundtrack, memes15:10 — Daily UA workflow: why “doing nothing” is sometimes the best move16:40 — When to switch to value optimization (signals + payer depth)18:00 — Live walkthrough: building a Meta campaign step-by-step19:20 — Final advice ---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.me---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.meIf you are interested in getting UA tips every week on Monday, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠lancaric.substack.com⁠⁠⁠⁠⁠⁠ & sign up for the Brutally Honest newsletter by Matej Lancaric

Wie baust du Mobile Games, die nicht nur Spaß machen, sondern auch auf jeder Plattform funktionieren und sich selbst tragen? In dieser Episode sprechen wir über Mobile Gaming: von der Idee über den Game Loop bis zur Monetarisierung. Mit dabei ist Fabi Fink, Game Lead bei Lotum. Lotum steht für Social Casual und Puzzle Hits wie Quiz Planet und Word Blitz, hat die Marke von 1 Milliarde Installationen geknackt und spielt technisch die gesamte Klaviatur von Web bis Native.Wir klären, warum Mobile inzwischen rund die Hälfte des Gaming-Umsatzes ausmacht und ordnen Hypercasual, Casual, Midcore und Hardcore mit vielen Beispielen ein. Wir zeigen, was Mobile heute bedeutet: Native Apps in App Store und Play Store, aber auch Games als Facebook Instant Games sowie Integrationen für Reddit, Discord, TikTok und Netflix. Du erfährst, wie Social Loops auf Plattformen funktionieren, warum asynchrones Multiplayer ein Growth-Hebel ist und was Viralität gegenüber klassischer User Acquisition auszeichnet.Technisch gehen wir tief rein: Warum Lotum für viele Titel auf Vue.js setzt und Game-UX wie eine hochinteraktive Web-App denkt. Wir sprechen über Performance-Details, GPU-freundliche Animationen und warum beim WordBlitz-Core Plain JavaScript die Nase vorn hat. Im Backend wird es handfest mit WebSockets, Redis-Clustern und Realtime-Events in der Google Cloud. Dazu kommen Tools und Plattformen wie Nakama (Open Source Backend for Games) und SpacetimeDB, plus eine ehrliche Kostenstory rund um Firebase.Natürlich geht es auch ums Geld: Ads vs. In-App Purchases, Hybrid-Modelle, ROAS über 180 Tage und was erfolgreiche Titel wirklich auszeichnet. Wir teilen KPI-Realität, A/B-Testing-Erkenntnisse, warum kleine UX-Texte große Effekte haben können und welche Schwelle ein Spiel bei Lotum erreichen sollte, um weiterverfolgt zu werden.Wenn du wissen willst, wie moderne Mobile Games entstehen – technologisch, produktseitig und monetär – schnapp dir diese Episode.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

Hosts Tor and Chet are joined by Adarsh Fernando, a Product Manager, and Ray Buse, a Software Engineer, to discuss Journeys for Android Studio. Powered by Gemini's vision and reasoning, Journeys aims to simplify end-to-end test creation and maintenance by converting the natural language you provide to describe the steps and assertions for each test, resulting in actions and evaluations performed directly on your app. Chapters: 0:00 - Intro 1:46 - Journeys: New AI-powered testing approach 3:40 - How Journeys Works with Gemini 4:27 - The natural language advantage 5:49 - Real-world use case: Google Maps 6:53 - Debugging with AI reasoning 8:08 - Why Journeys is important: Bridging the testing gap 9:56 - Journeys and End-to-End Testing 12:18 - Performance and Cached Journeys 24:14 - Android Studio and Firebase integration 25:27 - The development workflow 31:22 - AI for everyone: Beyond end-to-end testing 33:28 - Looking ahead: Feedback and the future  Resources: Journeys for Android Studio → https://goo.gle/4m9YOr3 App Testing (Android) → https://goo.gle/3HVKTqB   Tor on Bluesky → https://goo.gle/3ViCAYS Chet on Bluesky → https://goo.gle/4gzpccM Ardash on Bluesky → https://goo.gle/47JGNw9

Her gün dünyada ortalama 250 milyon mobil uygulama indiriliyor.Ama araştırmalara göre bu uygulamaların %75'i ilk 3 gün içinde hiç açılmıyor.Yani insanlar indiriyor… ama kullanmıyor.Bu da markalar için hem ciddi bir bütçe kaybı hem de yanlış stratejinin açık göstergesi.Bu bölümde mobil uygulama reklamlarının perde arkasına iniyoruz.Google Ads, Meta ve TikTok gibi platformlarda uygulama indirme kampanyaları nasıl çalışır, neden bazı uygulamalar viral olurken diğerleri sessizce silinir, gerçekten işe yarayan stratejiler neler — hepsini detaylarıyla konuşuyoruz.Uygulama Reklamlarının Görünmeyen GerçekleriBirçok işletme uygulamasını tanıtmak için “App Install” kampanyaları açıyor ama asıl hatayı tam burada yapıyor.Çünkü mobil reklamcılıkta başarı sadece “indirme sayısı” değil, kullanıcının uygulamayı açma, kayıt olma ve işlem yapma oranıyla ölçülür.Eğer kullanıcı sadece indirip uygulamayı hiç açmıyorsa, o kampanya görünürde başarılı ama gerçekte zarar ettiriyor.Ben bu bölümde size kampanyaların nasıl daha akıllı çalışacağını, SDK'nin neden en kritik parça olduğunu, uygulama içi event'leri nasıl kurmanız gerektiğini ve algoritmanın gerçekten “kaliteli kullanıcı”yı nasıl öğrendiğini anlatıyorum.Ayrıca “kayıt olma”, “sepete ekleme” veya “satın alma” gibi davranış bazlı hedeflerin, indirme odaklı kampanyalardan nasıl çok daha yüksek ROI getirdiğini örneklerle açıklıyorum.App Store ve Play Store Optimizasyonu (ASO)Mobil reklamcılıkta çoğu kişi sadece reklam tarafına odaklanıyor, ancak işin %40'ı aslında mağaza sayfasında kazanılıyor.Bir kullanıcı reklamdan etkilenip uygulama sayfasına geldiğinde ilk 3 saniye içinde ikna olmalı.İşte o an, uygulamanın ekran görüntüleri, açıklama metni ve yorumları devreye giriyor.Bu bölümde App Store optimizasyonunun indirme oranlarına etkisini, doğru görsel seçiminin ve açıklama metinlerinin nasıl fark yarattığını konuşuyoruz.Küçük bir ipucu: farklı ikon ve görselleri A/B test etmek bazen maliyeti %20'ye kadar düşürebiliyor.Kampanya Stratejileri ve Hedefleme İpuçlarıEn hızlı indirme kampanyasını kurmak için önce doğru kampanya tipini seçmek gerekiyor.Google Ads tarafında “App Campaigns” üç farklı hedefleme tipi sunar: Install Volume, In-App Actions ve Value Optimization.Hangisini, ne zaman ve nasıl kullanmanız gerektiğini adım adım anlatıyorum.Ayrıca Meta Ads ve TikTok tarafında performansı artıran video formatlarını, CTA (Call to Action) tetikleyicilerini ve kullanıcı davranışına göre farklı mesaj kurgularını paylaşıyorum.Gerçek Kullanıcı Kazanmanın FormülüSadece indirme değil, kullanıcıyı elde tutmak da bir strateji işidir.Retention rate, LTV ve ROI gibi metrikler artık her kampanyanın omurgası.Bir uygulamanın başarısı ilk 7 gün değil, 30. gün aktif kullanıcı oranıyla ölçülür.Bu yüzden bu bölümde sadece indirmeyi değil, sadakat yaratmayı da konuşuyoruz.Uygulama reklamlarında yapılan en büyük 5 hatayı, doğru hedefleme stratejilerini ve veriye dayalı büyüme yaklaşımlarını örneklerle aktarıyorum.Veri, Ölçümleme ve MMP AraçlarıKampanyalarınızı yönetirken veriyi doğru analiz etmek her şeydir.Bu yüzden Firebase, AppsFlyer, Adjust veya Singular gibi ölçümleme araçlarının ne işe yaradığını ve neden zorunlu olduklarını detaylandırıyorum.Bu araçlar sayesinde hangi reklam kanalının gerçekten satış getirdiğini, hangi platformun kullanıcıyı elde tuttuğunu net biçimde görebilirsiniz.Yani “ucuz indirme” değil, değerli kullanıcı odaklı büyüme mümkün hale geliyor.Sonuç: Gerçek Başarı Ne İndirmede Ne Bütçede, DavranıştaBu bölüm, mobil uygulama sahipleri, reklam yöneticileri ve performans pazarlamacılar için tam anlamıyla bir rehber niteliğinde.Reklam bütçenizi boşa harcamadan nasıl daha fazla indirme elde edeceğinizi, kullanıcı davranışlarını analiz ederek nasıl sadakat yaratabileceğinizi öğrenmek istiyorsanız bu bölümü mutlaka dinleyin.

Arnaud et Guillaume explore l'évolution de l'écosystème Java avec Java 25, Spring Boot et Quarkus, ainsi que les dernières tendances en intelligence artificielle avec les nouveaux modèles comme Grok 4 et Claude Code. Les animateurs font également le point sur l'infrastructure cloud, les défis MCP et CLI, tout en discutant de l'impact de l'IA sur la productivité des développeurs et la gestion de la dette technique. Enregistré le 8 août 2025 Téléchargement de l'épisode LesCastCodeurs-Episode–329.mp3 ou en vidéo sur YouTube. News Langages Java 25: JEP 515 : Profilage de méthode en avance (Ahead-of-Time) https://openjdk.org/jeps/515 Le JEP 515 a pour but d'améliorer le temps de démarrage et de chauffe des applications Java. L'idée est de collecter les profils d'exécution des méthodes lors d'une exécution antérieure, puis de les rendre immédiatement disponibles au démarrage de la machine virtuelle. Cela permet au compilateur JIT de générer du code natif dès le début, sans avoir à attendre que l'application soit en cours d'exécution. Ce changement ne nécessite aucune modification du code des applications, des bibliothèques ou des frameworks. L'intégration se fait via les commandes de création de cache AOT existantes. Voir aussi https://openjdk.org/jeps/483 et https://openjdk.org/jeps/514 Java 25: JEP 518 : Échantillonnage coopératif JFR https://openjdk.org/jeps/518 Le JEP 518 a pour objectif d'améliorer la stabilité et l'évolutivité de la fonction JDK Flight Recorder (JFR) pour le profilage d'exécution. Le mécanisme d'échantillonnage des piles d'appels de threads Java est retravaillé pour s'exécuter uniquement à des safepoints, ce qui réduit les risques d'instabilité. Le nouveau modèle permet un parcours de pile plus sûr, notamment avec le garbage collector ZGC, et un échantillonnage plus efficace qui prend en charge le parcours de pile concurrent. Le JEP ajoute un nouvel événement, SafepointLatency, qui enregistre le temps nécessaire à un thread pour atteindre un safepoint. L'approche rend le processus d'échantillonnage plus léger et plus rapide, car le travail de création de traces de pile est délégué au thread cible lui-même. Librairies Spring Boot 4 M1 https://spring.io/blog/2025/07/24/spring-boot–4–0–0-M1-available-now Spring Boot 4.0.0-M1 met à jour de nombreuses dépendances internes et externes pour améliorer la stabilité et la compatibilité. Les types annotés avec @ConfigurationProperties peuvent maintenant référencer des types situés dans des modules externes grâce à @ConfigurationPropertiesSource. Le support de l'information sur la validité des certificats SSL a été simplifié, supprimant l'état WILL_EXPIRE_SOON au profit de VALID. L'auto-configuration des métriques Micrometer supporte désormais l'annotation @MeterTag sur les méthodes annotées @Counted et @Timed, avec évaluation via SpEL. Le support de @ServiceConnection pour MongoDB inclut désormais l'intégration avec MongoDBAtlasLocalContainer de Testcontainers. Certaines fonctionnalités et API ont été dépréciées, avec des recommandations pour migrer les points de terminaison personnalisés vers les versions Spring Boot 2. Les versions milestones et release candidates sont maintenant publiées sur Maven Central, en plus du repository Spring traditionnel. Un guide de migration a été publié pour faciliter la transition depuis Spring Boot 3.5 vers la version 4.0.0-M1. Passage de Spring Boot à Quarkus : retour d'expérience https://blog.stackademic.com/we-switched-from-spring-boot-to-quarkus-heres-the-ugly-truth-c8a91c2b8c53 Une équipe a migré une application Java de Spring Boot vers Quarkus pour gagner en performances et réduire la consommation mémoire. L'objectif était aussi d'optimiser l'application pour le cloud natif. La migration a été plus complexe que prévu, notamment à cause de l'incompatibilité avec certaines bibliothèques et d'un écosystème Quarkus moins mature. Il a fallu revoir du code et abandonner certaines fonctionnalités spécifiques à Spring Boot. Les gains en performances et en mémoire sont réels, mais la migration demande un vrai effort d'adaptation. La communauté Quarkus progresse, mais le support reste limité comparé à Spring Boot. Conclusion : Quarkus est intéressant pour les nouveaux projets ou ceux prêts à être réécrits, mais la migration d'un projet existant est un vrai défi. LangChain4j 1.2.0 : Nouvelles fonctionnalités et améliorations https://github.com/langchain4j/langchain4j/releases/tag/1.2.0 Modules stables : Les modules langchain4j-anthropic, langchain4j-azure-open-ai, langchain4j-bedrock, langchain4j-google-ai-gemini, langchain4j-mistral-ai et langchain4j-ollama sont désormais en version stable 1.2.0. Modules expérimentaux : La plupart des autres modules de LangChain4j sont en version 1.2.0-beta8 et restent expérimentaux/instables. BOM mis à jour : Le langchain4j-bom a été mis à jour en version 1.2.0, incluant les dernières versions de tous les modules. Principales améliorations : Support du raisonnement/pensée dans les modèles. Appels d'outils partiels en streaming. Option MCP pour exposer automatiquement les ressources en tant qu'outils. OpenAI : possibilité de définir des paramètres de requête personnalisés et d'accéder aux réponses HTTP brutes et aux événements SSE. Améliorations de la gestion des erreurs et de la documentation. Filtering Metadata Infinispan ! (cc Katia( Et 1.3.0 est déjà disponible https://github.com/langchain4j/langchain4j/releases/tag/1.3.0 2 nouveaux modules expérimentaux, langchain4j-agentic et langchain4j-agentic-a2a qui introduisent un ensemble d'abstractions et d'utilitaires pour construire des applications agentiques Infrastructure Cette fois c'est vraiment l'année de Linux sur le desktop ! https://www.lesnumeriques.com/informatique/c-est-enfin-arrive-linux-depasse-un-seuil-historique-que-microsoft-pensait-intouchable-n239977.html Linux a franchi la barre des 5% aux USA Cette progression s'explique en grande partie par l'essor des systèmes basés sur Linux dans les environnements professionnels, les serveurs, et certains usages grand public. Microsoft, longtemps dominant avec Windows, voyait ce seuil comme difficilement atteignable à court terme. Le succès de Linux est également alimenté par la popularité croissante des distributions open source, plus légères, personnalisables et adaptées à des usages variés. Le cloud, l'IoT, et les infrastructures de serveurs utilisent massivement Linux, ce qui contribue à cette augmentation globale. Ce basculement symbolique marque un changement d'équilibre dans l'écosystème des systèmes d'exploitation. Toutefois, Windows conserve encore une forte présence dans certains segments, notamment chez les particuliers et dans les entreprises classiques. Cette évolution témoigne du dynamisme et de la maturité croissante des solutions Linux, devenues des alternatives crédibles et robustes face aux offres propriétaires. Cloud Cloudflare 1.1.1.1 s'en va pendant une heure d'internet https://blog.cloudflare.com/cloudflare–1–1–1–1-incident-on-july–14–2025/ Le 14 juillet 2025, le service DNS public Cloudflare 1.1.1.1 a subi une panne majeure de 62 minutes, rendant le service indisponible pour la majorité des utilisateurs mondiaux. Cette panne a aussi causé une dégradation intermittente du service Gateway DNS. L'incident est survenu suite à une mise à jour de la topologie des services Cloudflare qui a activé une erreur de configuration introduite en juin 2025. Cette erreur faisait que les préfixes destinés au service 1.1.1.1 ont été accidentellement inclus dans un nouveau service de localisation des données (Data Localization Suite), ce qui a perturbé le routage anycast. Le résultat a été une incapacité pour les utilisateurs à résoudre les noms de domaine via 1.1.1.1, rendant la plupart des services Internet inaccessibles pour eux. Ce n'était pas le résultat d'une attaque ou d'un problème BGP, mais une erreur interne de configuration. Cloudflare a rapidement identifié la cause, corrigé la configuration et mis en place des mesures pour prévenir ce type d'incident à l'avenir. Le service est revenu à la normale après environ une heure d'indisponibilité. L'incident souligne la complexité et la sensibilité des infrastructures anycast et la nécessité d'une gestion rigoureuse des configurations réseau. Web L'évolution des bonnes pratiques de Node.js https://kashw1n.com/blog/nodejs–2025/ Évolution de Node.js en 2025 : Le développement se tourne vers les standards du web, avec moins de dépendances externes et une meilleure expérience pour les développeurs. ES Modules (ESM) par défaut : Remplacement de CommonJS pour un meilleur outillage et une standardisation avec le web. Utilisation du préfixe node: pour les modules natifs afin d'éviter les conflits. API web intégrées : fetch, AbortController, et AbortSignal sont maintenant natifs, réduisant le besoin de librairies comme axios. Runner de test intégré : Plus besoin de Jest ou Mocha pour la plupart des cas. Inclut un mode “watch” et des rapports de couverture. Patterns asynchrones avancés : Utilisation plus poussée de async/await avec Promise.all() pour le parallélisme et les AsyncIterators pour les flux d'événements. Worker Threads pour le parallélisme : Pour les tâches lourdes en CPU, évitant de bloquer l'event loop principal. Expérience de développement améliorée : Intégration du mode --watch (remplace nodemon) et du support --env-file (remplace dotenv). Sécurité et performance : Modèle de permission expérimental pour restreindre l'accès et des hooks de performance natifs pour le monitoring. Distribution simplifiée : Création d'exécutables uniques pour faciliter le déploiement d'applications ou d'outils en ligne de commande. Sortie de Apache EChart 6 après 12 ans ! https://echarts.apache.org/handbook/en/basics/release-note/v6-feature/ Apache ECharts 6.0 : Sortie officielle après 12 ans d'évolution. 12 mises à niveau majeures pour la visualisation de données. Trois dimensions clés d'amélioration : Présentation visuelle plus professionnelle : Nouveau thème par défaut (design moderne). Changement dynamique de thème. Prise en charge du mode sombre. Extension des limites de l'expression des données : Nouveaux types de graphiques : Diagramme de cordes (Chord Chart), Nuage de points en essaim (Beeswarm Chart). Nouvelles fonctionnalités : Jittering pour nuages de points denses, Axes coupés (Broken Axis). Graphiques boursiers améliorés Liberté de composition : Nouveau système de coordonnées matriciel. Séries personnalisées améliorées (réutilisation du code, publication npm). Nouveaux graphiques personnalisés inclus (violon, contour, etc.). Optimisation de l'agencement des étiquettes d'axe. Data et Intelligence Artificielle Grok 4 s'est pris pour un nazi à cause des tools https://techcrunch.com/2025/07/15/xai-says-it-has-fixed-grok–4s-problematic-responses/ À son lancement, Grok 4 a généré des réponses offensantes, notamment en se surnommant « MechaHitler » et en adoptant des propos antisémites. Ce comportement provenait d'une recherche automatique sur le web qui a mal interprété un mème viral comme une vérité. Grok alignait aussi ses réponses controversées sur les opinions d'Elon Musk et de xAI, ce qui a amplifié les biais. xAI a identifié que ces dérapages étaient dus à une mise à jour interne intégrant des instructions encourageant un humour offensant et un alignement avec Musk. Pour corriger cela, xAI a supprimé le code fautif, remanié les prompts système, et imposé des directives demandant à Grok d'effectuer une analyse indépendante, en utilisant des sources diverses. Grok doit désormais éviter tout biais, ne plus adopter un humour politiquement incorrect, et analyser objectivement les sujets sensibles. xAI a présenté ses excuses, précisant que ces dérapages étaient dus à un problème de prompt et non au modèle lui-même. Cet incident met en lumière les défis persistants d'alignement et de sécurité des modèles d'IA face aux injections indirectes issues du contenu en ligne. La correction n'est pas qu'un simple patch technique, mais un exemple des enjeux éthiques et de responsabilité majeurs dans le déploiement d'IA à grande échelle. Guillaume a sorti toute une série d'article sur les patterns agentiques avec le framework ADK pour Java https://glaforge.dev/posts/2025/07/29/mastering-agentic-workflows-with-adk-the-recap/ Un premier article explique comment découper les tâches en sous-agents IA : https://glaforge.dev/posts/2025/07/23/mastering-agentic-workflows-with-adk-sub-agents/ Un deuxième article détaille comment organiser les agents de manière séquentielle : https://glaforge.dev/posts/2025/07/24/mastering-agentic-workflows-with-adk-sequential-agent/ Un troisième article explique comment paralleliser des tâches indépendantes : https://glaforge.dev/posts/2025/07/25/mastering-agentic-workflows-with-adk-parallel-agent/ Et enfin, comment faire des boucles d'amélioration : https://glaforge.dev/posts/2025/07/28/mastering-agentic-workflows-with-adk-loop-agents/ Tout ça évidemment en Java :slightly_smiling_face: 6 semaines de code avec Claude https://blog.puzzmo.com/posts/2025/07/30/six-weeks-of-claude-code/ Orta partage son retour après 6 semaines d'utilisation quotidienne de Claude Code, qui a profondément changé sa manière de coder. Il ne « code » plus vraiment ligne par ligne, mais décrit ce qu'il veut, laisse Claude proposer une solution, puis corrige ou ajuste. Cela permet de se concentrer sur le résultat plutôt que sur l'implémentation, comme passer de la peinture au polaroid. Claude s'avère particulièrement utile pour les tâches de maintenance : migrations, refactors, nettoyage de code. Il reste toujours en contrôle, révise chaque diff généré, et guide l'IA via des prompts bien cadrés. Il note qu'il faut quelques semaines pour prendre le bon pli : apprendre à découper les tâches et formuler clairement les attentes. Les tâches simples deviennent quasi instantanées, mais les tâches complexes nécessitent encore de l'expérience et du discernement. Claude Code est vu comme un très bon copilote, mais ne remplace pas le rôle du développeur qui comprend l'ensemble du système. Le gain principal est une vitesse de feedback plus rapide et une boucle d'itération beaucoup plus courte. Ce type d'outil pourrait bien redéfinir la manière dont on pense et structure le développement logiciel à moyen terme. Claude Code et les serveurs MCP : ou comment transformer ton terminal en assistant surpuissant https://touilleur-express.fr/2025/07/27/claude-code-et-les-serveurs-mcp-ou-comment-transformer-ton-terminal-en-assistant-surpuissant/ Nicolas continue ses études sur Claude Code et explique comment utiliser les serveurs MCP pour rendre Claude bien plus efficace. Le MCP Context7 montre comment fournir à l'IA la doc technique à jour (par exemple, Next.js 15) pour éviter les hallucinations ou les erreurs. Le MCP Task Master, autre serveur MCP, transforme un cahier des charges (PRD) en tâches atomiques, estimées, et organisées sous forme de plan de travail. Le MCP Playwright permet de manipuler des navigateurs et d'executer des tests E2E Le MCP Digital Ocean permet de déployer facilement l'application en production Tout n'est pas si ideal, les quotas sont atteints en quelques heures sur une petite application et il y a des cas où il reste bien plus efficace de le faire soit-même (pour un codeur expérimenté) Nicolas complète cet article avec l'écriture d'un MVP en 20 heures: https://touilleur-express.fr/2025/07/30/comment-jai-code-un-mvp-en-une-vingtaine-dheures-avec-claude-code/ Le développement augmenté, un avis politiquement correct, mais bon… https://touilleur-express.fr/2025/07/31/le-developpement-augmente-un-avis-politiquement-correct-mais-bon/ Nicolas partage un avis nuancé (et un peu provoquant) sur le développement augmenté, où l'IA comme Claude Code assiste le développeur sans le remplacer. Il rejette l'idée que cela serait « trop magique » ou « trop facile » : c'est une évolution logique de notre métier, pas un raccourci pour les paresseux. Pour lui, un bon dev reste celui qui structure bien sa pensée, sait poser un problème, découper, valider — même si l'IA aide à coder plus vite. Il raconte avoir codé une app OAuth, testée, stylisée et déployée en quelques heures, sans jamais quitter le terminal grâce à Claude. Ce genre d'outillage change le rapport au temps : on passe de « je vais y réfléchir » à « je tente tout de suite une version qui marche à peu près ». Il assume aimer cette approche rapide et imparfaite : mieux vaut une version brute livrée vite qu'un projet bloqué par le perfectionnisme. L'IA est selon lui un super stagiaire : jamais fatigué, parfois à côté de la plaque, mais diablement productif quand bien briefé. Il conclut que le « dev augmenté » ne remplace pas les bons développeurs… mais les développeurs moyens doivent s'y mettre, sous peine d'être dépassés. ChatGPT lance le mode d'étude : un apprentissage interactif pas à pas https://openai.com/index/chatgpt-study-mode/ OpenAI propose un mode d'étude dans ChatGPT qui guide les utilisateurs pas à pas plutôt que de donner directement la réponse. Ce mode vise à encourager la réflexion active et l'apprentissage en profondeur. Il utilise des instructions personnalisées pour poser des questions et fournir des explications adaptées au niveau de l'utilisateur. Le mode d'étude favorise la gestion de la charge cognitive et stimule la métacognition. Il propose des réponses structurées pour faciliter la compréhension progressive des sujets. Disponible dès maintenant pour les utilisateurs connectés, ce mode sera intégré dans ChatGPT Edu. L'objectif est de transformer ChatGPT en un véritable tuteur numérique, aidant les étudiants à mieux assimiler les connaissances. A priori Gemini viendrait de sortir un fonctionnalité similaire Lancement de GPT-OSS par OpenAI https://openai.com/index/introducing-gpt-oss/ https://openai.com/index/gpt-oss-model-card/ OpenAI a lancé GPT-OSS, sa première famille de modèles open-weight depuis GPT–2. Deux modèles sont disponibles : gpt-oss–120b et gpt-oss–20b, qui sont des modèles mixtes d'experts conçus pour le raisonnement et les tâches d'agent. Les modèles sont distribués sous licence Apache 2.0, permettant leur utilisation et leur personnalisation gratuites, y compris pour des applications commerciales. Le modèle gpt-oss–120b est capable de performances proches du modèle OpenAI o4-mini, tandis que le gpt-oss–20b est comparable au o3-mini. OpenAI a également open-sourcé un outil de rendu appelé Harmony en Python et Rust pour en faciliter l'adoption. Les modèles sont optimisés pour fonctionner localement et sont pris en charge par des plateformes comme Hugging Face et Ollama. OpenAI a mené des recherches sur la sécurité pour s'assurer que les modèles ne pouvaient pas être affinés pour des utilisations malveillantes dans les domaines biologique, chimique ou cybernétique. Anthropic lance Opus 4.1 https://www.anthropic.com/news/claude-opus–4–1 Anthropic a publié Claude Opus 4.1, une mise à jour de son modèle de langage. Cette nouvelle version met l'accent sur l'amélioration des performances en codage, en raisonnement et sur les tâches de recherche et d'analyse de données. Le modèle a obtenu un score de 74,5 % sur le benchmark SWE-bench Verified, ce qui représente une amélioration par rapport à la version précédente. Il excelle notamment dans la refactorisation de code multifichier et est capable d'effectuer des recherches approfondies. Claude Opus 4.1 est disponible pour les utilisateurs payants de Claude, ainsi que via l'API, Amazon Bedrock et Vertex AI de Google Cloud, avec des tarifs identiques à ceux d'Opus 4. Il est présenté comme un remplacement direct de Claude Opus 4, avec des performances et une précision supérieures pour les tâches de programmation réelles. OpenAI Summer Update. GPT–5 is out https://openai.com/index/introducing-gpt–5/ Détails https://openai.com/index/gpt–5-new-era-of-work/ https://openai.com/index/introducing-gpt–5-for-developers/ https://openai.com/index/gpt–5-safe-completions/ https://openai.com/index/gpt–5-system-card/ Amélioration majeure des capacités cognitives - GPT‑5 montre un niveau de raisonnement, d'abstraction et de compréhension nettement supérieur aux modèles précédents. Deux variantes principales - gpt-5-main : rapide, efficace pour les tâches générales. gpt-5-thinking : plus lent mais spécialisé dans les tâches complexes, nécessitant réflexion profonde. Routeur intelligent intégré - Le système sélectionne automatiquement la version la plus adaptée à la tâche (rapide ou réfléchie), sans intervention de l'utilisateur. Fenêtre de contexte encore étendue - GPT‑5 peut traiter des volumes de texte plus longs (jusqu'à 1 million de tokens dans certaines versions), utile pour des documents ou projets entiers. Réduction significative des hallucinations - GPT‑5 donne des réponses plus fiables, avec moins d'erreurs inventées ou de fausses affirmations. Comportement plus neutre et moins sycophant - Il a été entraîné pour mieux résister à l'alignement excessif avec les opinions de l'utilisateur. Capacité accrue à suivre des instructions complexes - GPT‑5 comprend mieux les consignes longues, implicites ou nuancées. Approche “Safe completions” - Remplacement des “refus d'exécution” par des réponses utiles mais sûres — le modèle essaie de répondre avec prudence plutôt que bloquer. Prêt pour un usage professionnel à grande échelle - Optimisé pour le travail en entreprise : rédaction, programmation, synthèse, automatisation, gestion de tâches, etc. Améliorations spécifiques pour le codage - GPT‑5 est plus performant pour l'écriture de code, la compréhension de contextes logiciels complexes, et l'usage d'outils de développement. Expérience utilisateur plus rapide et fluide- Le système réagit plus vite grâce à une orchestration optimisée entre les différents sous-modèles. Capacités agentiques renforcées - GPT‑5 peut être utilisé comme base pour des agents autonomes capables d'accomplir des objectifs avec peu d'interventions humaines. Multimodalité maîtrisée (texte, image, audio) - GPT‑5 intègre de façon plus fluide la compréhension de formats multiples, dans un seul modèle. Fonctionnalités pensées pour les développeurs - Documentation plus claire, API unifiée, modèles plus transparents et personnalisables. Personnalisation contextuelle accrue - Le système s'adapte mieux au style, ton ou préférences de l'utilisateur, sans instructions répétées. Utilisation énergétique et matérielle optimisée - Grâce au routeur interne, les ressources sont utilisées plus efficacement selon la complexité des tâches. Intégration sécurisée dans les produits ChatGPT - Déjà déployé dans ChatGPT avec des bénéfices immédiats pour les utilisateurs Pro et entreprises. Modèle unifié pour tous les usages - Un seul système capable de passer de la conversation légère à des analyses scientifiques ou du code complexe. Priorité à la sécurité et à l'alignement - GPT‑5 a été conçu dès le départ pour minimiser les abus, biais ou comportements indésirables. Pas encore une AGI - OpenAI insiste : malgré ses capacités impressionnantes, GPT‑5 n'est pas une intelligence artificielle générale. Non, non, les juniors ne sont pas obsolètes malgré l'IA ! (dixit GitHub) https://github.blog/ai-and-ml/generative-ai/junior-developers-arent-obsolete-heres-how-to-thrive-in-the-age-of-ai/ L'IA transforme le développement logiciel, mais les développeurs juniors ne sont pas obsolètes. Les nouveaux apprenants sont bien positionnés, car déjà familiers avec les outils IA. L'objectif est de développer des compétences pour travailler avec l'IA, pas d'être remplacé. La créativité et la curiosité sont des qualités humaines clés. Cinq façons de se démarquer : Utiliser l'IA (ex: GitHub Copilot) pour apprendre plus vite, pas seulement coder plus vite (ex: mode tuteur, désactiver l'autocomplétion temporairement). Construire des projets publics démontrant ses compétences (y compris en IA). Maîtriser les workflows GitHub essentiels (GitHub Actions, contribution open source, pull requests). Affûter son expertise en révisant du code (poser des questions, chercher des patterns, prendre des notes). Déboguer plus intelligemment et rapidement avec l'IA (ex: Copilot Chat pour explications, corrections, tests). Ecrire son premier agent IA avec A2A avec WildFly par Emmanuel Hugonnet https://www.wildfly.org/news/2025/08/07/Building-your-First-A2A-Agent/ Protocole Agent2Agent (A2A) : Standard ouvert pour l'interopérabilité universelle des agents IA. Permet communication et collaboration efficaces entre agents de différents fournisseurs/frameworks. Crée des écosystèmes multi-agents unifiés, automatisant les workflows complexes. Objet de l'article : Guide pour construire un premier agent A2A (agent météo) dans WildFly. Utilise A2A Java SDK pour Jakarta Servers, WildFly AI Feature Pack, un LLM (Gemini) et un outil Python (MCP). Agent conforme A2A v0.2.5. Prérequis : JDK 17+, Apache Maven 3.8+, IDE Java, Google AI Studio API Key, Python 3.10+, uv. Étapes de construction de l'agent météo : Création du service LLM : Interface Java (WeatherAgent) utilisant LangChain4J pour interagir avec un LLM et un outil Python MCP (fonctions get_alerts, get_forecast). Définition de l'agent A2A (via CDI) : ▪︎ Agent Card : Fournit les métadonnées de l'agent (nom, description, URL, capacités, compétences comme “weather_search”). Agent Executor : Gère les requêtes A2A entrantes, extrait le message utilisateur, appelle le service LLM et formate la réponse. Exposition de l'agent : Enregistrement d'une application JAX-RS pour les endpoints. Déploiement et test : Configuration de l'outil A2A-inspector de Google (via un conteneur Podman). Construction du projet Maven, configuration des variables d'environnement (ex: GEMINI_API_KEY). Lancement du serveur WildFly. Conclusion : Transformation minimale d'une application IA en agent A2A. Permet la collaboration et le partage d'informations entre agents IA, indépendamment de leur infrastructure sous-jacente. Outillage IntelliJ IDEa bouge vers une distribution unifiée https://blog.jetbrains.com/idea/2025/07/intellij-idea-unified-distribution-plan/ À partir de la version 2025.3, IntelliJ IDEA Community Edition ne sera plus distribuée séparément. Une seule version unifiée d'IntelliJ IDEA regroupera les fonctionnalités des éditions Community et Ultimate. Les fonctionnalités avancées de l'édition Ultimate seront accessibles via abonnement. Les utilisateurs sans abonnement auront accès à une version gratuite enrichie par rapport à l'édition Community actuelle. Cette unification vise à simplifier l'expérience utilisateur et réduire les différences entre les éditions. Les utilisateurs Community seront automatiquement migrés vers cette nouvelle version unifiée. Il sera possible d'activer les fonctionnalités Ultimate temporairement d'un simple clic. En cas d'expiration d'abonnement Ultimate, l'utilisateur pourra continuer à utiliser la version installée avec un jeu limité de fonctionnalités gratuites, sans interruption. Ce changement reflète l'engagement de JetBrains envers l'open source et l'adaptation aux besoins de la communauté. Prise en charge des Ancres YAML dans GitHub Actions https://github.com/actions/runner/issues/1182#issuecomment–3150797791 Afin d'éviter de dupliquer du contenu dans un workflow les Ancres permettent d'insérer des morceaux réutilisables de YAML Fonctionnalité attendue depuis des années et disponible chez GitLab depuis bien longtemps. Elle a été déployée le 4 aout. Attention à ne pas en abuser car la lisibilité de tels documents n'est pas si facile Gemini CLI rajoute les custom commands comme Claude https://cloud.google.com/blog/topics/developers-practitioners/gemini-cli-custom-slash-commands Mais elles sont au format TOML, on ne peut donc pas les partager avec Claude :disappointed: Automatiser ses workflows IA avec les hooks de Claude Code https://blog.gitbutler.com/automate-your-ai-workflows-with-claude-code-hooks/ Claude Code propose des hooks qui permettent d'exécuter des scripts à différents moments d'une session, par exemple au début, lors de l'utilisation d'outils, ou à la fin. Ces hooks facilitent l'automatisation de tâches comme la gestion de branches Git, l'envoi de notifications, ou l'intégration avec d'autres outils. Un exemple simple est l'envoi d'une notification sur le bureau à la fin d'une session. Les hooks se configurent via trois fichiers JSON distincts selon le scope : utilisateur, projet ou local. Sur macOS, l'envoi de notifications nécessite une permission spécifique via l'application “Script Editor”. Il est important d'avoir une version à jour de Claude Code pour utiliser ces hooks. GitButler permet desormais de s'intégrer à Claude Code via ces hooks: https://blog.gitbutler.com/parallel-claude-code/ Le client Git de Jetbrains bientot en standalone https://lp.jetbrains.com/closed-preview-for-jetbrains-git-client/ Demandé par certains utilisateurs depuis longtemps Ca serait un client graphique du même style qu'un GitButler, SourceTree, etc Apache Maven 4 …. arrive …. l'utilitaire mvnupva vous aider à upgrader https://maven.apache.org/tools/mvnup.html Fixe les incompatibilités connues Nettoie les redondances et valeurs par defaut (versions par ex) non utiles pour Maven 4 Reformattage selon les conventions maven … Une GitHub Action pour Gemini CLI https://blog.google/technology/developers/introducing-gemini-cli-github-actions/ Google a lancé Gemini CLI GitHub Actions, un agent d'IA qui fonctionne comme un “coéquipier de code” pour les dépôts GitHub. L'outil est gratuit et est conçu pour automatiser des tâches de routine telles que le triage des problèmes (issues), l'examen des demandes de tirage (pull requests) et d'autres tâches de développement. Il agit à la fois comme un agent autonome et un collaborateur que les développeurs peuvent solliciter à la demande, notamment en le mentionnant dans une issue ou une pull request. L'outil est basé sur la CLI Gemini, un agent d'IA open-source qui amène le modèle Gemini directement dans le terminal. Il utilise l'infrastructure GitHub Actions, ce qui permet d'isoler les processus dans des conteneurs séparés pour des raisons de sécurité. Trois flux de travail (workflows) open-source sont disponibles au lancement : le triage intelligent des issues, l'examen des pull requests et la collaboration à la demande. Pas besoin de MCP, le code est tout ce dont vous avez besoin https://lucumr.pocoo.org/2025/7/3/tools/ Armin souligne qu'il n'est pas fan du protocole MCP (Model Context Protocol) dans sa forme actuelle : il manque de composabilité et exige trop de contexte. Il remarque que pour une même tâche (ex. GitHub), utiliser le CLI est souvent plus rapide et plus efficace en termes de contexte que passer par un serveur MCP. Selon lui, le code reste la solution la plus simple et fiable, surtout pour automatiser des tâches répétitives. Il préfère créer des scripts clairs plutôt que se reposer sur l'inférence LLM : cela facilite la vérification, la maintenance et évite les erreurs subtiles. Pour les tâches récurrentes, si on les automatise, mieux vaut le faire avec du code reusable, plutôt que de laisser l'IA deviner à chaque fois. Il illustre cela en convertissant son blog entier de reStructuredText à Markdown : plutôt qu'un usage direct d'IA, il a demandé à Claude de générer un script complet, avec parsing AST, comparaison des fichiers, validation et itération. Ce workflow LLM→code→LLM (analyse et validation) lui a donné confiance dans le résultat final, tout en conservant un contrôle humain sur le processus. Il juge que MCP ne permet pas ce type de pipeline automatisé fiable, car il introduit trop d'inférence et trop de variations par appel. Pour lui, coder reste le meilleur moyen de garder le contrôle, la reproductibilité et la clarté dans les workflows automatisés. MCP vs CLI … https://www.async-let.com/blog/my-take-on-the-mcp-verses-cli-debate/ Cameron raconte son expérience de création du serveur XcodeBuildMCP, qui lui a permis de mieux comprendre le débat entre servir l'IA via MCP ou laisser l'IA utiliser directement les CLI du système. Selon lui, les CLIs restent préférables pour les développeurs experts recherchant contrôle, transparence, performance et simplicité. Mais les serveurs MCP excellent sur les workflows complexes, les contextes persistants, les contraintes de sécurité, et facilitent l'accès pour les utilisateurs moins expérimentés. Il reconnaît la critique selon laquelle MCP consomme trop de contexte (« context bloat ») et que les appels CLI peuvent être plus rapides et compréhensibles. Toutefois, il souligne que beaucoup de problèmes proviennent de la qualité des implémentations clients, pas du protocole MCP en lui‑même. Pour lui, un bon serveur MCP peut proposer des outils soigneusement définis qui simplifient la vie de l'IA (par exemple, renvoyer des données structurées plutôt que du texte brut à parser). Il apprécie la capacité des MCP à offrir des opérations état‑durables (sessions, mémoire, logs capturés), ce que les CLI ne gèrent pas naturellement. Certains scénarios ne peuvent pas fonctionner via CLI (pas de shell accessible) alors que MCP, en tant que protocole indépendant, reste utilisable par n'importe quel client. Son verdict : pas de solution universelle — chaque contexte mérite d'être évalué, et on ne devrait pas imposer MCP ou CLI à tout prix. Jules, l'agent de code asynchrone gratuit de Google, est sorti de beta et est disponible pour tout le monde https://blog.google/technology/google-labs/jules-now-available/ Jules, agent de codage asynchrone, est maintenant publiquement disponible. Propulsé par Gemini 2.5 Pro. Phase bêta : 140 000+ améliorations de code et retours de milliers de développeurs. Améliorations : interface utilisateur, corrections de bugs, réutilisation des configurations, intégration GitHub Issues, support multimodal. Gemini 2.5 Pro améliore les plans de codage et la qualité du code. Nouveaux paliers structurés : Introductif, Google AI Pro (limites 5x supérieures), Google AI Ultra (limites 20x supérieures). Déploiement immédiat pour les abonnés Google AI Pro et Ultra, incluant les étudiants éligibles (un an gratuit de AI Pro). Architecture Valoriser la réduction de la dette technique : un vrai défi https://www.lemondeinformatique.fr/actualites/lire-valoriser-la-reduction-de-la-dette-technique-mission-impossible–97483.html La dette technique est un concept mal compris et difficile à valoriser financièrement auprès des directions générales. Les DSI ont du mal à mesurer précisément cette dette, à allouer des budgets spécifiques, et à prouver un retour sur investissement clair. Cette difficulté limite la priorisation des projets de réduction de dette technique face à d'autres initiatives jugées plus urgentes ou stratégiques. Certaines entreprises intègrent progressivement la gestion de la dette technique dans leurs processus de développement. Des approches comme le Software Crafting visent à améliorer la qualité du code pour limiter l'accumulation de cette dette. L'absence d'outils adaptés pour mesurer les progrès rend la démarche encore plus complexe. En résumé, réduire la dette technique reste une mission délicate qui nécessite innovation, méthode et sensibilisation en interne. Il ne faut pas se Mocker … https://martinelli.ch/why-i-dont-use-mocking-frameworks-and-why-you-might-not-need-them-either/ https://blog.tremblay.pro/2025/08/not-using-mocking-frmk.html L'auteur préfère utiliser des fakes ou stubs faits à la main plutôt que des frameworks de mocking comme Mockito ou EasyMock. Les frameworks de mocking isolent le code, mais entraînent souvent : Un fort couplage entre les tests et les détails d'implémentation. Des tests qui valident le mock plutôt que le comportement réel. Deux principes fondamentaux guident son approche : Favoriser un design fonctionnel, avec logique métier pure (fonctions sans effets de bord). Contrôler les données de test : par exemple en utilisant des bases réelles (via Testcontainers) plutôt que de simuler. Dans sa pratique, les seuls cas où un mock externe est utilisé concernent les services HTTP externes, et encore il préfère en simuler seulement le transport plutôt que le comportement métier. Résultat : les tests deviennent plus simples, plus rapides à écrire, plus fiables, et moins fragiles aux évolutions du code. L'article conclut que si tu conçois correctement ton code, tu pourrais très bien ne pas avoir besoin de frameworks de mocking du tout. Le blog en réponse d'Henri Tremblay nuance un peu ces retours Méthodologies C'est quoi être un bon PM ? (Product Manager) Article de Chris Perry, un PM chez Google : https://thechrisperry.substack.com/p/being-a-good-pm-at-google Le rôle de PM est difficile : Un travail exigeant, où il faut être le plus impliqué de l'équipe pour assurer le succès. 1. Livrer (shipper) est tout ce qui compte : La priorité absolue. Mieux vaut livrer et itérer rapidement que de chercher la perfection en théorie. Un produit livré permet d'apprendre de la réalité. 2. Donner l'envie du grand large : La meilleure façon de faire avancer un projet est d'inspirer l'équipe avec une vision forte et désirable. Montrer le “pourquoi”. 3. Utiliser son produit tous les jours : Non négociable pour réussir. Permet de développer une intuition et de repérer les vrais problèmes que la recherche utilisateur ne montre pas toujours. 4. Être un bon ami : Créer des relations authentiques et aider les autres est un facteur clé de succès à long terme. La confiance est la base d'une exécution rapide. 5. Donner plus qu'on ne reçoit : Toujours chercher à aider et à collaborer. La stratégie optimale sur la durée est la coopération. Ne pas être possessif avec ses idées. 6. Utiliser le bon levier : Pour obtenir une décision, il faut identifier la bonne personne qui a le pouvoir de dire “oui”, et ne pas se laisser bloquer par des avis non décisionnaires. 7. N'aller que là où on apporte de la valeur : Combler les manques, faire le travail ingrat que personne ne veut faire. Savoir aussi s'écarter (réunions, projets) quand on n'est pas utile. 8. Le succès a plusieurs parents, l'échec est orphelin : Si le produit réussit, c'est un succès d'équipe. S'il échoue, c'est la faute du PM. Il faut assumer la responsabilité finale. Conclusion : Le PM est un chef d'orchestre. Il ne peut pas jouer de tous les instruments, mais son rôle est d'orchestrer avec humilité le travail de tous pour créer quelque chose d'harmonieux. Tester des applications Spring Boot prêtes pour la production : points clés https://www.wimdeblauwe.com/blog/2025/07/30/how-i-test-production-ready-spring-boot-applications/ L'auteur (Wim Deblauwe) détaille comment il structure ses tests dans une application Spring Boot destinée à la production. Le projet inclut automatiquement la dépendance spring-boot-starter-test, qui regroupe JUnit 5, AssertJ, Mockito, Awaitility, JsonAssert, XmlUnit et les outils de testing Spring. Tests unitaires : ciblent les fonctions pures (record, utilitaire), testés simplement avec JUnit et AssertJ sans démarrage du contexte Spring. Tests de cas d'usage (use case) : orchestrent la logique métier, généralement via des use cases qui utilisent un ou plusieurs dépôts de données. Tests JPA/repository : vérifient les interactions avec la base via des tests realisant des opérations CRUD (avec un contexte Spring pour la couche persistance). Tests de contrôleur : permettent de tester les endpoints web (ex. @WebMvcTest), souvent avec MockBean pour simuler les dépendances. Tests d'intégration complets : ils démarrent tout le contexte Spring (@SpringBootTest) pour tester l'application dans son ensemble. L'auteur évoque également des tests d'architecture, mais sans entrer dans le détail dans cet article. Résultat : une pyramide de tests allant des plus rapides (unitaires) aux plus complets (intégration), garantissant fiabilité, vitesse et couverture sans surcharge inutile. Sécurité Bitwarden offre un serveur MCP pour que les agents puissent accéder aux mots de passe https://nerds.xyz/2025/07/bitwarden-mcp-server-secure-ai/ Bitwarden introduit un serveur MCP (Model Context Protocol) destiné à intégrer de manière sécurisée les agents IA dans les workflows de gestion de mots de passe. Ce serveur fonctionne en architecture locale (local-first) : toutes les interactions et les données sensibles restent sur la machine de l'utilisateur, garantissant l'application du principe de chiffrement zero‑knowledge. L'intégration se fait via l'interface CLI de Bitwarden, permettant aux agents IA de générer, récupérer, modifier et verrouiller les identifiants via des commandes sécurisées. Le serveur peut être auto‑hébergé pour un contrôle maximal des données. Le protocole MCP est un standard ouvert qui permet de connecter de façon uniforme des agents IA à des sources de données et outils tiers, simplifiant les intégrations entre LLM et applications. Une démo avec Claude (agent IA d'Anthropic) montre que l'IA peut interagir avec le coffre Bitwarden : vérifier l'état, déverrouiller le vault, générer ou modifier des identifiants, le tout sans intervention humaine directe. Bitwarden affiche une approche priorisant la sécurité, mais reconnaît les risques liés à l'utilisation d'IA autonome. L'usage d'un LLM local privé est fortement recommandé pour limiter les vulnérabilités. Si tu veux, je peux aussi te résumer les enjeux principaux (interopérabilité, sécurité, cas d'usage) ou un extrait spécifique ! NVIDIA a une faille de securite critique https://www.wiz.io/blog/nvidia-ai-vulnerability-cve–2025–23266-nvidiascape Il s'agit d'une faille d'évasion de conteneur dans le NVIDIA Container Toolkit. La gravité est jugée critique avec un score CVSS de 9.0. Cette vulnérabilité permet à un conteneur malveillant d'obtenir un accès root complet sur l'hôte. L'origine du problème vient d'une mauvaise configuration des hooks OCI dans le toolkit. L'exploitation peut se faire très facilement, par exemple avec un Dockerfile de seulement trois lignes. Le risque principal concerne la compromission de l'isolation entre différents clients sur des infrastructures cloud GPU partagées. Les versions affectées incluent toutes les versions du NVIDIA Container Toolkit jusqu'à la 1.17.7 et du NVIDIA GPU Operator jusqu'à la version 25.3.1. Pour atténuer le risque, il est recommandé de mettre à jour vers les dernières versions corrigées. En attendant, il est possible de désactiver certains hooks problématiques dans la configuration pour limiter l'exposition. Cette faille met en lumière l'importance de renforcer la sécurité des environnements GPU partagés et la gestion des conteneurs AI. Fuite de données de l'application Tea : points essentiels https://knowyourmeme.com/memes/events/the-tea-app-data-leak Tea est une application lancée en 2023 qui permet aux femmes de laisser des avis anonymes sur des hommes rencontrés. En juillet 2025, une importante fuite a exposé environ 72 000 images sensibles (selfies, pièces d'identité) et plus d'1,1 million de messages privés. La fuite a été révélée après qu'un utilisateur ait partagé un lien pour télécharger la base de données compromise. Les données touchées concernaient majoritairement des utilisateurs inscrits avant février 2024, date à laquelle l'application a migré vers une infrastructure plus sécurisée. En réponse, Tea prévoit de proposer des services de protection d'identité aux utilisateurs impactés. Faille dans le paquet npm is : attaque en chaîne d'approvisionnement https://socket.dev/blog/npm-is-package-hijacked-in-expanding-supply-chain-attack Une campagne de phishing ciblant les mainteneurs npm a compromis plusieurs comptes, incluant celui du paquet is. Des versions compromises du paquet is (notamment les versions 3.3.1 et 5.0.0) contenaient un chargeur de malware JavaScript destiné aux systèmes Windows. Ce malware a offert aux attaquants un accès à distance via WebSocket, permettant potentiellement l'exécution de code arbitraire. L'attaque fait suite à d'autres compromissions de paquets populaires comme eslint-config-prettier, eslint-plugin-prettier, synckit, @pkgr/core, napi-postinstall, et got-fetch. Tous ces paquets ont été publiés sans aucun commit ou PR sur leurs dépôts GitHub respectifs, signalant un accès non autorisé aux tokens mainteneurs. Le domaine usurpé [npnjs.com](http://npnjs.com) a été utilisé pour collecter les jetons d'accès via des emails de phishing trompeurs. L'épisode met en lumière la fragilité des chaînes d'approvisionnement logicielle dans l'écosystème npm et la nécessité d'adopter des pratiques renforcées de sécurité autour des dépendances. Revues de sécurité automatisées avec Claude Code https://www.anthropic.com/news/automate-security-reviews-with-claude-code Anthropic a lancé des fonctionnalités de sécurité automatisées pour Claude Code, un assistant de codage d'IA en ligne de commande. Ces fonctionnalités ont été introduites en réponse au besoin croissant de maintenir la sécurité du code alors que les outils d'IA accélèrent considérablement le développement de logiciels. Commande /security-review : les développeurs peuvent exécuter cette commande dans leur terminal pour demander à Claude d'identifier les vulnérabilités de sécurité, notamment les risques d'injection SQL, les vulnérabilités de script intersite (XSS), les failles d'authentification et d'autorisation, ainsi que la gestion non sécurisée des données. Claude peut également suggérer et implémenter des correctifs. Intégration GitHub Actions : une nouvelle action GitHub permet à Claude Code d'analyser automatiquement chaque nouvelle demande d'extraction (pull request). L'outil examine les modifications de code pour y trouver des vulnérabilités, applique des règles personnalisables pour filtrer les faux positifs et commente directement la demande d'extraction avec les problèmes détectés et les correctifs recommandés. Ces fonctionnalités sont conçues pour créer un processus d'examen de sécurité cohérent et s'intégrer aux pipelines CI/CD existants, ce qui permet de s'assurer qu'aucun code n'atteint la production sans un examen de sécurité de base. Loi, société et organisation Google embauche les personnes clés de Windsurf https://www.blog-nouvelles-technologies.fr/333959/openai-windsurf-google-deepmind-codage-agentique/ windsurf devait être racheté par OpenAI Google ne fait pas d'offre de rachat mais débauche quelques personnes clés de Windsurf Windsurf reste donc indépendante mais sans certains cerveaux y compris son PDG. Les nouveaux dirigeants sont les ex leaders des force de vente Donc plus une boîte tech Pourquoi le deal a 3 milliard est tombé à l'eau ? On ne sait pas mais la divergence et l‘indépendance technologique est possiblement en cause. Les transfuge vont bosser chez Deepmind dans le code argentique Opinion Article: https://www.linkedin.com/pulse/dear-people-who-think-ai-low-skilled-code-monkeys-future-jan-moser-svade/ Jan Moser critique ceux qui pensent que l'IA et les développeurs peu qualifiés peuvent remplacer les ingénieurs logiciels compétents. Il cite l'exemple de l'application Tea, une plateforme de sécurité pour femmes, qui a exposé 72 000 images d'utilisateurs en raison d'une mauvaise configuration de Firebase et d'un manque de pratiques de développement sécurisées. Il souligne que l'absence de contrôles automatisés et de bonnes pratiques de sécurité a permis cette fuite de données. Moser avertit que des outils comme l'IA ne peuvent pas compenser l'absence de compétences en génie logiciel, notamment en matière de sécurité, de gestion des erreurs et de qualité du code. Il appelle à une reconnaissance de la valeur des ingénieurs logiciels qualifiés et à une approche plus rigoureuse dans le développement logiciel. YouTube déploie une technologie d'estimation d'âge pour identifier les adolescents aux États-Unis https://techcrunch.com/2025/07/29/youtube-rolls-out-age-estimatation-tech-to-identify-u-s-teens-and-apply-additional-protections/ Sujet très à la mode, surtout au UK mais pas que… YouTube commence à déployer une technologie d'estimation d'âge basée sur l'IA pour identifier les utilisateurs adolescents aux États-Unis, indépendamment de l'âge déclaré lors de l'inscription. Cette technologie analyse divers signaux comportementaux, tels que l'historique de visionnage, les catégories de vidéos consultées et l'âge du compte. Lorsqu'un utilisateur est identifié comme adolescent, YouTube applique des protections supplémentaires, notamment : Désactivation des publicités personnalisées. Activation des outils de bien-être numérique, tels que les rappels de temps d'écran et de coucher. Limitation de la visualisation répétée de contenus sensibles, comme ceux liés à l'image corporelle. Si un utilisateur est incorrectement identifié comme mineur, il peut vérifier son âge via une pièce d'identité gouvernementale, une carte de crédit ou un selfie. Ce déploiement initial concerne un petit groupe d'utilisateurs aux États-Unis et sera étendu progressivement. Cette initiative s'inscrit dans les efforts de YouTube pour renforcer la sécurité des jeunes utilisateurs en ligne. Mistral AI : contribution à un standard environnemental pour l'IA https://mistral.ai/news/our-contribution-to-a-global-environmental-standard-for-ai Mistral AI a réalisé la première analyse de cycle de vie complète d'un modèle d'IA, en collaboration avec plusieurs partenaires. L'étude quantifie l'impact environnemental du modèle Mistral Large 2 sur les émissions de gaz à effet de serre, la consommation d'eau, et l'épuisement des ressources. La phase d'entraînement a généré 20,4 kilotonnes de CO₂ équivalent, consommé 281 000 m³ d'eau, et utilisé 660 kg SB-eq (mineral consumption). Pour une réponse de 400 tokens, l'impact marginal est faible mais non négligeable : 1,14 gramme de CO₂, 45 mL d'eau, et 0,16 mg d'équivalent antimoine. Mistral propose trois indicateurs pour évaluer cet impact : l'impact absolu de l'entraînement, l'impact marginal de l'inférence, et le ratio inference/impact total sur le cycle de vie. L'entreprise souligne l'importance de choisir le modèle en fonction du cas d'usage pour limiter l'empreinte environnementale. Mistral appelle à plus de transparence et à l'adoption de standards internationaux pour permettre une comparaison claire entre modèles. L'IA promettait plus d'efficacité… elle nous fait surtout travailler plus https://afterburnout.co/p/ai-promised-to-make-us-more-efficient Les outils d'IA devaient automatiser les tâches pénibles et libérer du temps pour les activités stratégiques et créatives. En réalité, le temps gagné est souvent aussitôt réinvesti dans d'autres tâches, créant une surcharge. Les utilisateurs croient être plus productifs avec l'IA, mais les données contredisent cette impression : une étude montre que les développeurs utilisant l'IA prennent 19 % de temps en plus pour accomplir leurs tâches. Le rapport DORA 2024 observe une baisse de performance globale des équipes lorsque l'usage de l'IA augmente : –1,5 % de throughput et –7,2 % de stabilité de livraison pour +25 % d'adoption de l'IA. L'IA ne réduit pas la charge mentale, elle la déplace : rédaction de prompts, vérification de résultats douteux, ajustements constants… Cela épuise et limite le temps de concentration réelle. Cette surcharge cognitive entraîne une forme de dette mentale : on ne gagne pas vraiment du temps, on le paie autrement. Le vrai problème vient de notre culture de la productivité, qui pousse à toujours vouloir optimiser, quitte à alimenter l'épuisement professionnel. Trois pistes concrètes : Repenser la productivité non en temps gagné, mais en énergie préservée. Être sélectif dans l'usage des outils IA, en fonction de son ressenti et non du battage médiatique. Accepter la courbe en J : l'IA peut être utile, mais nécessite des ajustements profonds pour produire des gains réels. Le vrai hack de productivité ? Parfois, ralentir pour rester lucide et durable. Conférences MCP Submit Europe https://mcpdevsummit.ai/ Retour de JavaOne en 2026 https://inside.java/2025/08/04/javaone-returns–2026/ JavaOne, la conférence dédiée à la communauté Java, fait son grand retour dans la Bay Area du 17 au 19 mars 2026. Après le succès de l'édition 2025, ce retour s'inscrit dans la continuité de la mission initiale de la conférence : rassembler la communauté pour apprendre, collaborer et innover. La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 25–27 août 2025 : SHAKA Biarritz - Biarritz (France) 5 septembre 2025 : JUG Summer Camp 2025 - La Rochelle (France) 12 septembre 2025 : Agile Pays Basque 2025 - Bidart (France) 15 septembre 2025 : Agile Tour Montpellier - Montpellier (France) 18–19 septembre 2025 : API Platform Conference - Lille (France) & Online 22–24 septembre 2025 : Kernel Recipes - Paris (France) 22–27 septembre 2025 : La Mélée Numérique - Toulouse (France) 23 septembre 2025 : OWASP AppSec France 2025 - Paris (France) 23–24 septembre 2025 : AI Engineer Paris - Paris (France) 25 septembre 2025 : Agile Game Toulouse - Toulouse (France) 25–26 septembre 2025 : Paris Web 2025 - Paris (France) 30 septembre 2025–1 octobre 2025 : PyData Paris 2025 - Paris (France) 2 octobre 2025 : Nantes Craft - Nantes (France) 2–3 octobre 2025 : Volcamp - Clermont-Ferrand (France) 3 octobre 2025 : DevFest Perros-Guirec 2025 - Perros-Guirec (France) 6–7 octobre 2025 : Swift Connection 2025 - Paris (France) 6–10 octobre 2025 : Devoxx Belgium - Antwerp (Belgium) 7 octobre 2025 : BSides Mulhouse - Mulhouse (France) 7–8 octobre 2025 : Agile en Seine - Issy-les-Moulineaux (France) 8–10 octobre 2025 : SIG 2025 - Paris (France) & Online 9 octobre 2025 : DevCon #25 : informatique quantique - Paris (France) 9–10 octobre 2025 : Forum PHP 2025 - Marne-la-Vallée (France) 9–10 octobre 2025 : EuroRust 2025 - Paris (France) 16 octobre 2025 : PlatformCon25 Live Day Paris - Paris (France) 16 octobre 2025 : Power 365 - 2025 - Lille (France) 16–17 octobre 2025 : DevFest Nantes - Nantes (France) 17 octobre 2025 : Sylius Con 2025 - Lyon (France) 17 octobre 2025 : ScalaIO 2025 - Paris (France) 17–19 octobre 2025 : OpenInfra Summit Europe - Paris (France) 20 octobre 2025 : Codeurs en Seine - Rouen (France) 23 octobre 2025 : Cloud Nord - Lille (France) 30–31 octobre 2025 : Agile Tour Bordeaux 2025 - Bordeaux (France) 30–31 octobre 2025 : Agile Tour Nantais 2025 - Nantes (France) 30 octobre 2025–2 novembre 2025 : PyConFR 2025 - Lyon (France) 4–7 novembre 2025 : NewCrafts 2025 - Paris (France) 5–6 novembre 2025 : Tech Show Paris - Paris (France) 6 novembre 2025 : dotAI 2025 - Paris (France) 6 novembre 2025 : Agile Tour Aix-Marseille 2025 - Gardanne (France) 7 novembre 2025 : BDX I/O - Bordeaux (France) 12–14 novembre 2025 : Devoxx Morocco - Marrakech (Morocco) 13 novembre 2025 : DevFest Toulouse - Toulouse (France) 15–16 novembre 2025 : Capitole du Libre - Toulouse (France) 19 novembre 2025 : SREday Paris 2025 Q4 - Paris (France) 19–21 novembre 2025 : Agile Grenoble - Grenoble (France) 20 novembre 2025 : OVHcloud Summit - Paris (France) 21 novembre 2025 : DevFest Paris 2025 - Paris (France) 27 novembre 2025 : DevFest Strasbourg 2025 - Strasbourg (France) 28 novembre 2025 : DevFest Lyon - Lyon (France) 1–2 décembre 2025 : Tech Rocks Summit 2025 - Paris (France) 4–5 décembre 2025 : Agile Tour Rennes - Rennes (France) 5 décembre 2025 : DevFest Dijon 2025 - Dijon (France) 9–11 décembre 2025 : APIdays Paris - Paris (France) 9–11 décembre 2025 : Green IO Paris - Paris (France) 10–11 décembre 2025 : Devops REX - Paris (France) 10–11 décembre 2025 : Open Source Experience - Paris (France) 11 décembre 2025 : Normandie.ai 2025 - Rouen (France) 28–31 janvier 2026 : SnowCamp 2026 - Grenoble (France) 2–6 février 2026 : Web Days Convention - Aix-en-Provence (France) 3 février 2026 : Cloud Native Days France 2026 - Paris (France) 12–13 février 2026 : Touraine Tech #26 - Tours (France) 22–24 avril 2026 : Devoxx France 2026 - Paris (France) 23–25 avril 2026 : Devoxx Greece - Athens (Greece) 17 juin 2026 : Devoxx Poland - Krakow (Poland) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

There's drama brewing between AI-answer engine company Perplexity and hosting platform Cloudflare, which recently declared it would actively block AI bots from crawling websites without the owners' permission. Cloudflare received complaints, set up its own test sites, and then asked Perplexity pointed questions only - and got answers! Not a great look, Perplexity.Two years after Vercel launched v1 of its AI SDK, it has dropped v5, and it's got some major improvements. Rebuilt chat hooks, improved tool calling, more agentic controls, and it works with React, Svelte, Vue, and Angular. That's just the tip of the iceberg, but it seems like Vercel's got a winner on its hands.Never one to rest on its laurels, the team behind the TanStack universe unveils TanStack DB. TanStack DB extends TanStack Query with collections, live queries and optimistic mutations client side for building super fast apps on sync without needing a Firebase subscriptionIn Lightning News this week, OpenAI released GTP-5. It's better at all the things: writing, coding, and health questions, but are the improvements going to be so great we'll actually notice? Time will tell. Also, in disappointing news, Cognition, the AI company that scooped up the remains of Windsurf, has laid off the Windsurf employees it acquired, or told those who remain to expect 80-hour, six day a week, work weeks: another way to effectively reduce headcount. Let's hope this doesn't set a new precedent in Silicon Valley.Timestamps:00:53 - Drama between Cloudflare and Perplexity09:15 - Vercel AI SDK 515:23 - TanStack DB update19:28 - GPT-525:23 - A not-so-great Windsurf & Cognition update30:51 - What's making us happyLinks:Paige - Vercel AI SDK 5Jack - TanStack DBTJ - Drama between Cloudflare and PerplexityOpenAI GPT-5 arrivesA not-great Windsurf & Cognition updateIllinois blocks AI from being your therapistA Guide To Hover And Pointer Media Queries (Smashing Magazine)Paige - The Retrievals podcastJack - Creality CR-Scan Otter 3D ScannerTJ - People I (Mostly) AdmineThanks as always to our sponsor, the Blue Collar Coder channel on YouTube. You can join us in our Discord channel, explore our website and reach us via email, or talk to us on X, Bluesky, or YouTube.Front-end Fire websiteBlue Collar Coder on YouTubeBlue Collar Coder on DiscordReach out via emailTweet at us on X @front_end_fireFollow us on Bluesky @front-end-fire.comSubscribe to our YouTube channel @Front-EndFirePodcast

While Brian frolics somewhere in the Pacific Northwest, Jason brings in cyber-sleuth Dave Bittner for a jam-packed episode covering everything from Gen X's slow descent into obscurity to furries, feds, and face-scanning your way into porn. The guys start with a salute to the late, great Tom Lehrer—a math nerd with a piano and zero tolerance for BS—before diving into the avalanche of cyber screwups plaguing today's digital circus.The biggest spill? The so-called “safe” dating app Tea just doxxed its entire user base—because who needs privacy when you've got bad Firebase settings from 2017? Meanwhile, teens are befriending chatbots, Microsoft is issuing pink slips via PowerPoint, and Meta might be training its AI on stolen porn. Add in farmers installing turnstiles in the Dolomites to keep influencers off their grass, age verification laws that Norman Reedus can bypass with a JPEG, and Tesla diners turning into 24/7 neighbor hellscapes, and yeah—it's just another week on the internet.If you're a Gen Xer feeling invisible, underpaid, and over it, congrats—you're not alone. This episode is a full buffet of schadenfreude, digital paranoia, and good old-fashioned grump. Pour a cup of whatever's not boiling, and tune in for the roast. Tom Lehrer would've approved.Sponsors:DeleteMe - Head over to JoinDeleteMe.com/GOG and use the code "GOG" for 20% off.Private Internet Access - Go to GOG.Show/vpn and sign up today. For a limited time only, you can get OUR favorite VPN for as little as $2.03 a month.SetApp - With a single monthly subscription you get 240+ apps for your Mac. Go to SetApp and get started today!!!1Password - Get a great deal on the only password manager recommended by Grumpy Old Geeks! gog.show/1passwordShow notes at https://gog.show/708FOLLOW UPWhy Gen X is the real loser generationTeens say they are turning to AI for friendshipIN THE NEWSHackers steal images from women's dating safety app that vets menHackers leak 13,000 user photos and IDs from the Tea app, designed as a women's safe spaceTea dating app disables direct messaging as it investigates data breachThe Tea App Data Breach: What Was Exposed and What We Know About the Class Action LawsuitTea App's Second Breach: 1.1 Million Private Messages Exposed in ...The Tea App Breach: A Catastrophic Privacy Failure in the Quest for ...Tea App Leak: What's Going on With the 4chan Tea App Data ...Tea app hacked: 13,000 photos leaked after 4chan call to actionThe Tea app hack explained – how a data breach spilled thousands of photos from the top free US app, and what to doWomen are reporting bad men on this app. Here's the legal tea on the app called TeaMajor Security Breach at Tea App Exposes Sensitive User DataThe dating app that doxxed 72,000 women... - YouTubeTea app fallout worsens as leaked selfies used in rating site, online ...Two data breaches in one week on social media site TeaDating safety app Tea suspends messaging after hack - BBCFirst Came Tea. Then Came the Male Rage.The Tea App Data Breach: What Was Exposed and What We Know ...How Tea's data breach became a brand momentTea app takes messaging system offline after security breachTea app hacked as women's photos, IDs & even DMs leaked onlineMicrosoft Releases List of Jobs Most and Least Likely to Be Replaced by AICopyright Lawsuit Accuses Meta of Pirating Adult Films for AI TrainingFed-up Italian farmers set up mountain turnstiles to charge access to Instagram hot spotsGrumpy Old Geeks recommend Private Internet AccessThe Age-Gated Internet Is HereSocial media age verification laws in the United States - WikipediaAll the loopholes people are using to get past the Online Safety ActAge Verification Laws Send VPN Use Soaring—and Threaten the Open InternetThe UK's new age-gating rules are easy to bypass - The VergeHow Minors Bypass Age Verification: 6 Common Methods to Watch ...Age Verification in the United States: Insights from the Open ...Age-Verification Evasion in 2025: How Minors Outsmart ... - Shufti ProExploring Privacy-Preserving Age Verification: A Close Look at Zero-Knowledge ProofsWhat to know about online age verification laws | AP NewsUS State age verification laws for adult content – AVPAAge verification tools on adult websites bypassed in secondsAge Verification - The Heritage FoundationAge Verification Bill Tracker - Free Speech CoalitionOnline Pornography Age Verification Laws by US State - KindbridgeOnline Age Verification Laws Could Do More Harm Than GoodUK probes 34 porn sites under new age-check rulesHow to Bypass US Porn Ban and Age Verification Laws - CybernewsWhy I Emphatically Oppose Online Age Verification MandatesReady or not, age verification is rolling out across the internetTesla partly liable in Florida Autopilot trial, jury awards $200M punitive damagesChatGPT users shocked to learn their chats were in Google search resultsLiving Next To Tesla Diner Is 'Absolute Hell,' Neighbors SaySongs and Lyrics by Tom LehrerTHE DARK SIDE WITH DAVEDave BittnerThe CyberWireHacking HumansCaveatControl LoopOnly Malware in the BuildingFurries and SecurityTom Lehrer was the face of the real 1950sTom Lehrer Full Copenhagen PerformanceThe delightful story of a prank Tom Leher played on the NSAPeter SchickeleInsta360 X5The History of Hollywood's Large Format Film Cameras!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Securing Firebase: Lessons Re-Learned from the Tea Breach Inspried by the breach of the Tea app, Brendon Evans recorded a video to inform of Firebase security issues https://isc.sans.edu/diary/Securing%20Firebase%3A%20Lessons%20Re-Learned%20from%20the%20Tea%20Breach/32158 WebKit Vulnerability Exploited before Apple Patch A WebKit vulnerablity patched by Apple yesterday has already been exploited in Google Chrome. Google noted the exploit with its patch for the same vulnerability in Chrome. https://nvd.nist.gov/vuln/detail/CVE-2025-6558 Scattered Spider Update CISA released an update for its report on Scattered Spider, noting that the group also calls helpdesks impersonating users, not just the other way around. https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-320a

Germany's Tech-Driven Warfare & Ethical ImplicationsGermany is integrating AI, robotics, and human-machine teaming into its military, deploying tech like robotic cockroaches for surveillance and mini-robots for urban combat. These innovations aim to enhance decision-making and minimize human risk. Yet, critics warn of ethical and legal concerns, especially around loss of human oversight in lethal decisions. Despite official claims that humans will remain in control, the autonomy debate continues.Astronomer's "Kiss Cam" ScandalA viral Coldplay concert “Kiss Cam” captured Astronomer's CEO and Chief People Officer—both married—trying to avoid public display. The clip, viewed over 127 million times, sparked privacy concerns and led to their resignations. In a PR twist, Astronomer hired Gwyneth Paltrow (ex-wife of Coldplay's Chris Martin) as a temporary spokesperson to steer attention back to the company's data automation services.Tea App's Privacy BreachesThe women's dating safety app “Tea” was compromised twice. First, 72,000 private images, including IDs and selfies, were leaked due to an unsecured Firebase database. A second breach exposed over a million sensitive messages containing personal info and taboo topics. Despite promises of anonymity, users' names, social links, and phone numbers were often easily traceable—defeating the app's core promise of safety.WhoFi and the Future of SurveillanceWhoFi, a surveillance system developed at La Sapienza University, uses Wi-Fi distortions (Channel State Information) to uniquely identify individuals based on their body's impact on signal patterns. Achieving up to 95.5% accuracy, it can track people without phones or devices, raising serious privacy concerns about ubiquitous, passive surveillance with no opt-out.ChatGPT Agent Bypasses SecurityOpenAI's ChatGPT Agent demonstrated it can bypass Cloudflare's anti-bot “I am not a robot” checks. Operating in a sandboxed browser environment, it navigated multi-step verifications without CAPTCHAs. This challenges the efficacy of current web security protocols and signals that anti-bot measures may be obsolete in the face of advanced AI agents.AI-Driven Pricing Controversy in AirlinesAmerican Airlines' CEO slammed Delta for using AI in airfare pricing, labeling it “bait and switch.” Delta claims uniform pricing across channels and denies tailoring fares per customer. While Delta plans broader AI deployment, competitors like Southwest and American reject AI pricing, citing privacy concerns and potential fare manipulation.Clorox Hack & Vendor NegligenceA 2023 cyberattack cost Clorox $380 million due to a security lapse by its IT vendor, Cognizant. Hackers impersonated Clorox employees and tricked service desk agents into resetting credentials—no identity checks were performed. Now, Clorox is suing Cognizant for damages stemming from this avoidable breach.North Korean Espionage via Remote WorkNorth Korean operatives used stolen identities to land remote IT jobs at major U.S. firms like Nike and Chick-fil-A. Aided by VPNs and paid stand-ins for interviews, they funneled salaries to the regime. A U.S. woman received 8.5 years in prison for facilitating this scheme, which exposed sensitive company data and posed national security risks.

In this episode of The New Stack Agents, Andrew Lee, co-founder of Shortwave and Firebase, discusses the evolution of his Gmail-centric email client into an AI-first platform. Initially launched in 2020 with traditional improvements like better threading and search, Shortwave pivoted to agentic AI after the rise of large language models (LLMs). Early features like summarization and translation garnered hype but lacked deep utility. However, as models improved in 2023—especially Anthropic's Claude Sonnet 3.5—Shortwave leaned heavily into tool-calling agents that could execute complex, multi-step tasks autonomously. Lee notes Anthropic's lead in this area, especially in chaining tools intelligently, unlike earlier models from OpenAI. Still, challenges remain with managing large numbers of tools without breaking model reasoning. Looking ahead, Lee envisions AI that can take proactive actions—like responding to emails—and dynamically generate interfaces tailored to tasks in real-time. This shift could fundamentally reshape how productivity apps work, with Shortwave aiming to be at the forefront of that transformation.Learn more from The New Stack about the latest insights of the power AI at scale:Why Streaming Is the Power Grid for AI-Native Data PlatformsCompanies Must Embrace BeSpoke AI Designed for IT WorkflowsJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

Episode SummaryIn this episode of Player Driven, host Greg engages in an insightful conversation with Josh Plotnek, Head of Content at Keewano, diving deep into the significance of actionable insights derived from game data analytics. The discussion highlights how understanding player behavior through data can drastically improve player experience and game performance. Josh shares practical tips for studios of all sizes, emphasizing how AI-driven analytics can help uncover hidden issues, transforming raw data into meaningful actions.Guest Information Name: Josh Plotnek Role: Head of Content at Keewano Expertise: Data Analytics, Content Strategy, Game Development Insights Company: KeewanoAbout KeewanoKeewano is a groundbreaking analytics platform that leverages advanced AI to provide actionable insights into player behavior, enabling gaming studios of any size to enhance player experience, retention, and engagement.Key Takeaways Data Alone Isn't Enough (07:35)Collecting data is the starting point; real value comes from understanding the "why" behind player behavior to make impactful decisions. Recognizing Frustration vs. Engagement (20:08)Player frustration can be either positive (engaging) or negative (leading to churn). Analyzing "recovery behaviors" helps studios differentiate and respond effectively. Start Small, Then Scale Your Analytics (14:39)Smaller studios can utilize accessible tools like Unity Analytics and Firebase, gradually scaling to more sophisticated AI-driven analytics solutions as they grow. Leveraging AI to Uncover Hidden Issues (11:27)Advanced analytics powered by AI can identify complex problems within games, such as pinpointing an item missed in earlier levels that causes significant churn later on. Future of Analytics: Conversational and Accessible (49:34)The future of data analytics is making complex insights conversational, allowing anyone on the team to ask direct questions and receive clear, actionable answers.Resources Mentioned Keewano Blog Unity Analytics FirebaseListen and LearnTune into the full episode to discover more about turning your game data into powerful insights and actionable strategies to enhance player satisfaction and loyalty.

This week we're joined by Stepan Parunashvili, co-creator of InstantDB, a new database that's designed to make it easier to build local-first apps. Instant is a replacement for Firebase, and it's designed to be a more modern, more flexible, and more powerful database for the modern web. Join us as we dive into the details of InstantDB, the challenges of building a new database, and the future of local-first development.This episode is sponsored by WorkOS (https://workos.com)https://www.linkedin.com/in/stepan-parunashvili-65698932/https://www.instantdb.com/https://github.com/instantdb/instanthttps://github.com/stopachka

In this episode, we sit down with the team behind Airflux, the AI-powered ad optimizer from Airbridge, and unpack how it's quietly changing the rules of mobile game monetization.

In this episode of Project Synapse, join our group of AI-obsessed IT professionals as they discuss the intriguing concept of AI-generated code, specifically focusing on 'vibe coding.' Marcel Gagne, a former system administrator turned author, dives deep into the history and potential of writing code through AI. The episode covers the evolution from early programming languages like COBOL and Fortran to modern AI coding tools like Cursor and Firebase. Discover how AI tools aid in prototyping, personal productivity, and the future possibilities in enterprise-level applications. The team also explores security implications, testing methodologies, and the importance of responsible AI use in development. Tune in to learn about the present and future impact of AI on programming and systems development. 00:00 Introduction to Project Synapse 00:36 Meet the Hosts 02:53 The Evolution of Programming Languages 08:27 The Rise of Vibe Coding 14:00 Practical Applications and Experiences 19:54 Advanced Tools and IDEs 22:39 Challenges and Solutions in AI Coding 29:56 Starting Fresh: The Importance of Context 33:04 Introduction to Programming by Kenny Rogers 33:28 Legendary Programmer John Carmack 33:38 AI in Game Development 34:19 Nostalgia for Classic Games 35:59 The Evolution of Game Engines 38:04 AI's Role in Modern Coding 38:36 Proof of Concept and Rapid Prototyping 44:20 Security Concerns with AI-Generated Code 50:06 The Future of AI in Enterprise Systems 01:00:27 The Importance of Testing and Security 01:03:44 Final Thoughts and Recommendations

Dozens of AI updates.

Werner Jainek and Vojtěch Rylko from Cultured Code talk about their migration of Things Cloud to Server Side with Swift and what they learned along the way.GuestThings - To-Do List for Mac & iOSThings (@things.app) — BlueskyThings (@culturedcode)Things (@things@mastodon.online)Werner Jainek (@jainek@mastodon.social)Vojtěch RylkoVojtech Rylko (@vry@mastodon.social)Vojtěch Rylko | LinkedInVojtěch Rylko (@vojtechrylko)vojtarylko (Vojtech Rylko)AnnouncementsJoin Bushel BetaJoin our Patreon!Newsletters | BrightDigitLinksSwift.org - How Swift's server support powers Things CloudThe Success Story of Server-Side Swift at Cultured Code - Vojtech Rylko - YouTubeRelated EpisodesSwift on Android with Marc Prud'hommeauxSwift, Server Side, Serverless with Sébastien StormacqFull Stack Lyriq with Adegboyega OlusunmadePixelBlitz in Public with Martin LasekSwiftly Tooling with Pol Piella AbadiaBackend Decisions with Mikaela CaronWhat is Firebase with Peter FrieseAWS and SOTO with Adam FowlerSocial MediaEmailleo@brightdigit.comGitHub - @brightdigitTwitter BrightDigit - @brightdigitLeo - @leogdionLinkedInBrightDigitLeoPatreon - brightdigitCreditsMusic from https://filmmusic.io"Blippy Trance" by Kevin MacLeod (https://incompetech.com)License: CC BY (http://creativecommons.org/licenses/by/4.0/) (00:00) - Overview of Cultured Code and Things App (02:19) - Migrating to Server-Side Swift (09:07) - Technical Challenges and Solutions (27:56) - Background Workers and Swift (32:11) - Swift 6 Adoption (36:34) - Chaos Testing and Deployment Thanks to our monthly supporters Tomáš Slíž Edward Sanchez Steven Lipton ★ Support this podcast on Patreon ★

Welcome to Now in Android, your ongoing guide to what's new and notable in the world of Android development. In this episode, we'll cover the return of Google I/O, Android Studio Turning 10, the Android 16 Betas, Imagen in Firebase, the latest in AndroidX, and more! For links to these items, check out Now in Android #114 on Medium → https://goo.gle/4hA69xv  Catch the latest episode of #TheAndroidShow here → https://goo.gle/tas-mar25  Watch more Now in Android → https://goo.gle/now-in-android  Subscribe to Android Developers → https://goo.gle/AndroidDevs  

In this conversation, Simon Grimm interviews Dohyun Kim, known as YourAverageTechBro, about his journey as an app developer and content creator. They discuss the challenges and successes in building apps, the importance of marketing, and the technologies used in app development, including React Native, Supabase, and AI tools. Dohyun shares insights on his most successful app, Montee, and the strategies behind its development and marketing, as well as the lessons learned from previous projects. In this conversation, Dohyun discusses the development of his app, Montee, focusing on the use of Next.js and Supabase for differentiation and backend management. He shares insights on API security, handling costs, and user management strategies. The importance of action bias in development is emphasized, along with ideation and keyword research strategies. The discussion also covers social media marketing tactics and preferences between web and mobile app development.Learn React Native - https://galaxies.devDohyun KimYouTube: https://www.youtube.com/@YourAverageTechBroTikTok: https://www.tiktok.com/@youraveragetechbroInstagram: https://www.instagram.com/youraveragetechbroX: https://x.com/youravgtechbroLinksMontee: https://www.montee.aiPerfect Interview: https://www.perfectinterview.aiGemini: https://ai.google.dev/TakeawaysDohyun prefers using technologies that allow for rapid development and shipping.He believes in copying successful ideas rather than focusing on originality.Montee, his AI meeting recorder app, achieved $1,500 in monthly recurring revenue shortly after launch.Dohyun discusses the challenges of app growth and the impact of churn on revenue.He highlights the importance of effective marketing strategies for app success.Dohyun prefers Supabase over Firebase for its relational database capabilities and better documentation.He shares insights on the technology stack used for PerfectInterview.ai, including Next.js and Gemini.Dohyun believes that app growth is often a series of step functions rather than exponential growth. Copy first and differentiate second is a key strategy.API keys should never be exposed in client-side code.User requests should always be traceable to prevent abuse.Action bias is crucial for shipping apps.Keyword research is not the only way to ideate apps.Social media marketing can drive app visibility.Instagram is currently more explosive for growth than TikTok.Web apps allow for faster updates and cash flow management.Developers should focus on building value-adding features.It's important to distinguish between fun projects and income-generating apps.

The Deep Linking Tool You Need for Seamless User Journeys!

Fredrik snackar med Oskar Wahlbäck om att bygga och testa idéer, så snabbt och ofta som möjligt. Och med hjälp av språkmodeller, för att kunna få mer gjort snabbare utan att behöva dra in fler utvecklare. Språkmodeller har blivit en naturlig och viktig del av Oskars process, och han berättar hur han arbetar med och tänker kring det. Oskar berättar mycket om hur han jobbat med olika produkter och idéer, och hur han arbetar och tänker för att så snabbt som möjligt både se om en idé är bra utan också om den kan få några kunder. Att fråga mamma är, tyvärr, inte rätt väg framåt. Vad är du beredd att göra för att testa en idé? Var medveten om det, och anpassa därefter. Är det en skyldighet att göra något du faktiskt vill göra? Ett stort tack till Cloudnet som sponsrar vår VPS! Har du kommentarer, frågor eller tips? Vi är @kodsnack, @thieta, @krig, och @bjoreman på Mastodon, har en sida på Facebook och epostas på info@kodsnack.se om du vill skriva längre. Vi läser allt som skickas. Gillar du Kodsnack får du hemskt gärna recensera oss i iTunes! Du kan också stödja podden genom att ge oss en kaffe (eller två!) på Ko-fi, eller handla något i vår butik. Länkar Oskar Bokaklipp Way out west Beetroot Beetroot academy VBA - Visual basic for applications Firebase och Firestore Google cloud functions Cocurrency Transaktioner Stöd oss på Ko-fi Agil lokförare-klistermärket Glide Aquire - sida där man kan sälja tidiga startups och tjänster The mom test - bok Lean startup Flutterflow Fiverr Titlar Inte kodare från början En klassisk start Foodora fast i Burma Boka i kommentarerna Det hade inte ChatGPT heller tänkt på En app för dig själv Alla idéer kommer inte att funka Är det här etiskt? Du måste testa Våga börja Jag får göra det själv Göra i princip vad som helst Fallhöjden noll, för alla

Arnaud et Emmanuel discutent des nouvelles de ce mois. On y parle intégrité de JVM, fetch size de JDBC, MCP, de prompt engineering, de DeepSeek bien sûr mais aussi de Maven 4 et des proxy de répository Maven. Et d'autres choses encore, bonne lecture. Enregistré le 7 février 2025 Téléchargement de l'épisode LesCastCodeurs-Episode-322.mp3 ou en vidéo sur YouTube. News Langages Les evolutions de la JVM pour augmenter l'intégrité https://inside.java/2025/01/03/evolving-default-integrity/ un article sur les raisons pour lesquelles les editeurs de frameworks et les utilisateurs s'arrachent les cheveux et vont continuer garantir l'integrite du code et des données en enlevant des APIs existantes historiquemnt agents dynamiques, setAccessible, Unsafe, JNI Article expliques les risques percus par les mainteneurs de la JVM Franchement c'est un peu leg sur les causes l'article, auto propagande JavaScript Temporal, enfin une API propre et moderne pour gérer les dates en JS https://developer.mozilla.org/en-US/blog/javascript-temporal-is-coming/ JavaScript Temporal est un nouvel objet conçu pour remplacer l'objet Date, qui présente des défauts. Il résout des problèmes tels que le manque de prise en charge des fuseaux horaires et la mutabilité. Temporal introduit des concepts tels que les instants, les heures civiles et les durées. Il fournit des classes pour gérer diverses représentations de date/heure, y compris celles qui tiennent compte du fuseau horaire et celles qui n'en tiennent pas compte. Temporal simplifie l'utilisation de différents calendriers (par exemple, chinois, hébreu). Il comprend des méthodes pour les comparaisons, les conversions et le formatage des dates et des heures. La prise en charge par les navigateurs est expérimentale, Firefox Nightly ayant l'implémentation la plus aboutie. Un polyfill est disponible pour essayer Temporal dans n'importe quel navigateur. Librairies Un article sur les fetch size du JDBC et les impacts sur vos applications https://in.relation.to/2025/01/24/jdbc-fetch-size/ qui connait la valeur fetch size par default de son driver? en fonction de vos use cases, ca peut etre devastateur exemple d'une appli qui retourne 12 lignes et un fetch size de oracle a 10, 2 a/r pour rien et si c'est 50 lignres retournées la base de donnée est le facteur limitant, pas Java donc monter sont fetch size est avantageux, on utilise la memoire de Java pour eviter la latence Quarkus annouce les MCP servers project pour collecter les servier MCP en Java https://quarkus.io/blog/introducing-mcp-servers/ MCP d'Anthropic introspecteur de bases JDBC lecteur de filke system Dessine en Java FX demarrables facilement avec jbang et testes avec claude desktop, goose et mcp-cli permet d'utliser le pouvoir des librarires Java de votre IA d'ailleurs Spring a la version 0.6 de leur support MCP https://spring.io/blog/2025/01/23/spring-ai-mcp-0 Infrastructure Apache Flink sur Kibernetes https://www.decodable.co/blog/get-running-with-apache-flink-on-kubernetes-2 un article tres complet ejn deux parties sur l'installation de Flink sur Kubernetes installation, setup mais aussi le checkpointing, la HA, l'observablité Data et Intelligence Artificielle 10 techniques de prompt engineering https://medium.com/google-cloud/10-prompt-engineering-techniques-every-beginner-should-know-bf6c195916c7 Si vous voulez aller plus loin, l'article référence un très bon livre blanc sur le prompt engineering https://www.kaggle.com/whitepaper-prompt-engineering Les techniques évoquées : Zero-Shot Prompting: On demande directement à l'IA de répondre à une question sans lui fournir d'exemple préalable. C'est comme si on posait une question à une personne sans lui donner de contexte. Few-Shot Prompting: On donne à l'IA un ou plusieurs exemples de la tâche qu'on souhaite qu'elle accomplisse. C'est comme montrer à quelqu'un comment faire quelque chose avant de lui demander de le faire. System Prompting: On définit le contexte général et le but de la tâche pour l'IA. C'est comme donner à l'IA des instructions générales sur ce qu'elle doit faire. Role Prompting: On attribue un rôle spécifique à l'IA (enseignant, journaliste, etc.). C'est comme demander à quelqu'un de jouer un rôle spécifique. Contextual Prompting: On fournit des informations supplémentaires ou un contexte pour la tâche. C'est comme donner à quelqu'un toutes les informations nécessaires pour répondre à une question. Step-Back Prompting: On pose d'abord une question générale, puis on utilise la réponse pour poser une question plus spécifique. C'est comme poser une question ouverte avant de poser une question plus fermée. Chain-of-Thought Prompting: On demande à l'IA de montrer étape par étape comment elle arrive à sa conclusion. C'est comme demander à quelqu'un d'expliquer son raisonnement. Self-Consistency Prompting: On pose plusieurs fois la même question à l'IA et on compare les réponses pour trouver la plus cohérente. C'est comme vérifier une réponse en la posant sous différentes formes. Tree-of-Thoughts Prompting: On permet à l'IA d'explorer plusieurs chemins de raisonnement en même temps. C'est comme considérer toutes les options possibles avant de prendre une décision. ReAct Prompting: On permet à l'IA d'interagir avec des outils externes pour résoudre des problèmes complexes. C'est comme donner à quelqu'un les outils nécessaires pour résoudre un problème. Les patterns GenAI the thoughtworks https://martinfowler.com/articles/gen-ai-patterns/ tres introductif et pre RAG le direct prompt qui est un appel direct au LLM: limitations de connaissance et de controle de l'experience eval: evaluer la sortie d'un LLM avec plusieurs techniques mais fondamentalement une fonction qui prend la demande, la reponse et donc un score numerique evaluation via un LLM (le meme ou un autre), ou evaluation humaine tourner les evaluations a partir de la chaine de build amis aussi en live vu que les LLMs puvent evoluer. Decrit les embedding notament d'image amis aussi de texte avec la notion de contexte DeepSeek et la fin de la domination de NVidia https://youtubetranscriptoptimizer.com/blog/05_the_short_case_for_nvda un article sur les raisons pour lesquelles NVIDIA va se faire cahllengert sur ses marges 90% de marge quand meme parce que les plus gros GPU et CUDA qui est proprio mais des approches ardware alternatives existent qui sont plus efficientes (TPU et gros waffle) Google, MS et d'autres construisent leurs GPU alternatifs CUDA devient de moins en moins le linga franca avec l'investissement sur des langages intermediares alternatifs par Apple, Google OpenAI etc L'article parle de DeepSkeek qui est venu mettre une baffe dans le monde des LLMs Ils ont construit un competiteur a gpt4o et o1 avec 5M de dollars et des capacites de raisonnements impressionnant la cles c'etait beaucoup de trick d'optimisation mais le plus gros est d'avoir des poids de neurores sur 8 bits vs 32 pour les autres. et donc de quatizer au fil de l'eau et au moment de l'entrainement beaucoup de reinforcemnt learning innovatifs aussi et des Mixture of Expert donc ~50x moins chers que OpenAI Donc plus besoin de GPU qui on des tonnes de vRAM ah et DeepSeek est open source un article de semianalytics change un peu le narratif le papier de DeepSkeek en dit long via ses omissions par ensemple les 6M c'est juste l'inference en GPU, pas les couts de recherches et divers trials et erreurs en comparaison Claude Sonnet a coute 10M en infererence DeepSeek a beaucoup de CPU pre ban et ceratins post bans evalués a 5 Milliards en investissement. leurs avancées et leur ouverture reste extremement interessante Une intro à Apache Iceberg http://blog.ippon.fr/2025/01/17/la-revolution-des-donnees-lavenement-des-lakehouses-avec-apache-iceberg/ issue des limites du data lake. non structuré et des Data Warehouses aux limites en diversite de données et de volume entrent les lakehouse Et particulierement Apache Iceberg issue de Netflix gestion de schema mais flexible notion de copy en write vs merge on read en fonction de besoins garantie atomicite, coherence, isoliation et durabilite notion de time travel et rollback partitions cachées (qui abstraient la partition et ses transfos) et evolution de partitions compatbile avec les moteurs de calcul comme spark, trino, flink etc explique la structure des metadonnées et des données Guillaume s'amuse à générer des histoires courtes de Science-Fiction en programmant des Agents IA avec LangChain4j et aussi avec des workflows https://glaforge.dev/posts/2025/01/27/an-ai-agent-to-generate-short-scifi-stories/ https://glaforge.dev/posts/2025/01/31/a-genai-agent-with-a-real-workflow/ Création d'un générateur automatisé de nouvelles de science-fiction à l'aide de Gemini et Imagen en Java, LangChain4j, sur Google Cloud. Le système génère chaque nuit des histoires, complétées par des illustrations créées par le modèle Imagen 3, et les publie sur un site Web. Une étape d'auto-réflexion utilise Gemini pour sélectionner la meilleure image pour chaque chapitre. L'agent utilise un workflow explicite, drivé par le code Java, où les étapes sont prédéfinies dans le code, plutôt que de s'appuyer sur une planification basée sur LLM. Le code est disponible sur GitHub et l'application est déployée sur Google Cloud. L'article oppose les agents de workflow explicites aux agents autonomes, en soulignant les compromis de chaque approche. Car parfois, les Agent IA autonomes qui gèrent leur propre planning hallucinent un peu trop et n'établissent pas un plan correctement, ou ne le suive pas comme il faut, voire hallucine des “function call”. Le projet utilise Cloud Build, le Cloud Run jobs, Cloud Scheduler, Firestore comme base de données, et Firebase pour le déploiement et l'automatisation du frontend. Dans le deuxième article, L'approche est différente, Guillaume utilise un outil de Workflow, plutôt que de diriger le planning avec du code Java. L'approche impérative utilise du code Java explicite pour orchestrer le workflow, offrant ainsi un contrôle et une parallélisation précis. L'approche déclarative utilise un fichier YAML pour définir le workflow, en spécifiant les étapes, les entrées, les sorties et l'ordre d'exécution. Le workflow comprend les étapes permettant de générer une histoire avec Gemini 2, de créer une invite d'image, de générer des images avec Imagen 3 et d'enregistrer le résultat dans Cloud Firestore (base de donnée NoSQL). Les principaux avantages de l'approche impérative sont un contrôle précis, une parallélisation explicite et des outils de programmation familiers. Les principaux avantages de l'approche déclarative sont des définitions de workflow peut-être plus faciles à comprendre (même si c'est un YAML, berk !) la visualisation, l'évolutivité et une maintenance simplifiée (on peut juste changer le YAML dans la console, comme au bon vieux temps du PHP en prod). Les inconvénients de l'approche impérative incluent le besoin de connaissances en programmation, les défis potentiels en matière de maintenance et la gestion des conteneurs. Les inconvénients de l'approche déclarative incluent une création YAML pénible, un contrôle de parallélisation limité, l'absence d'émulateur local et un débogage moins intuitif. Le choix entre les approches dépend des exigences du projet, la déclarative étant adaptée aux workflows plus simples. L'article conclut que la planification déclarative peut aider les agents IA à rester concentrés et prévisibles. Outillage Vulnérabilité des proxy Maven https://github.blog/security/vulnerability-research/attacks-on-maven-proxy-repositories/ Quelque soit le langage, la techno, il est hautement conseillé de mettre en place des gestionnaires de repositories en tant que proxy pour mieux contrôler les dépendances qui contribuent à la création de vos produits Michael Stepankin de l'équipe GitHub Security Lab a cherché a savoir si ces derniers ne sont pas aussi sources de vulnérabilité en étudiant quelques CVEs sur des produits comme JFrog Artifactory, Sonatype Nexus, et Reposilite Certaines failles viennent de la UI des produits qui permettent d'afficher les artifacts (ex: mettez un JS dans un fichier POM) et même de naviguer dedans (ex: voir le contenu d'un jar / zip et on exploite l'API pour lire, voir modifier des fichiers du serveur en dehors des archives) Les artifacts peuvent aussi être compromis en jouant sur les paramètres propriétaires des URLs ou en jouant sur le nomage avec les encodings. Bref, rien n'est simple ni niveau. Tout système rajoute de la compléxité et il est important de les tenir à mettre à jour. Il faut surveiller activement sa chaine de distribution via différents moyens et ne pas tout miser sur le repository manager. L'auteur a fait une présentation sur le sujet : https://www.youtube.com/watch?v=0Z_QXtk0Z54 Apache Maven 4… Bientôt, c'est promis …. qu'est ce qu'il y aura dedans ? https://gnodet.github.io/maven4-presentation/ Et aussi https://github.com/Bukama/MavenStuff/blob/main/Maven4/whatsnewinmaven4.md Apache Maven 4 Doucement mais surement …. c'est le principe d'un projet Maven 4.0.0-rc-2 est dispo (Dec 2024). Maven a plus de 20 ans et est largement utilisé dans l'écosystème Java. La compatibilité ascendante a toujours été une priorité, mais elle a limité la flexibilité. Maven 4 introduit des changements significatifs, notamment un nouveau schéma de construction et des améliorations du code. Changements du POM Séparation du Build-POM et du Consumer-POM : Build-POM : Contient des informations propres à la construction (ex. plugins, configurations). Consumer-POM : Contient uniquement les informations nécessaires aux consommateurs d'artefacts (ex. dépendances). Nouveau Modèle Version 4.1.0 : Utilisé uniquement pour le Build-POM, alors que le Consumer-POM reste en 4.0.0 pour la compatibilité. Introduit de nouveaux éléments et en marque certains comme obsolètes. Modules renommés en sous-projets : “Modules” devient “Sous-projets” pour éviter la confusion avec les Modules Java. L'élément remplace (qui reste pris en charge). Nouveau type de packaging : “bom” (Bill of Materials) : Différencie les POMs parents et les BOMs de gestion des dépendances. Prend en charge les exclusions et les imports basés sur les classifiers. Déclaration explicite du répertoire racine : permet de définir explicitement le répertoire racine du projet. Élimine toute ambiguïté sur la localisation des racines de projet. Nouvelles variables de répertoire : ${project.rootDirectory}, ${session.topDirectory} et ${session.rootDirectory} pour une meilleure gestion des chemins. Remplace les anciennes solutions non officielles et variables internes obsolètes. Prise en charge de syntaxes alternatives pour le POM Introduction de ModelParser SPI permettant des syntaxes alternatives pour le POM. Apache Maven Hocon Extension est un exemple précoce de cette fonctionnalité. Améliorations pour les sous-projets Versioning automatique des parents Il n'est plus nécessaire de définir la version des parents dans chaque sous-projet. Fonctionne avec le modèle de version 4.1.0 et s'étend aux dépendances internes au projet. Support complet des variables compatibles CI Le Flatten Maven Plugin n'est plus requis. Prend en charge les variables comme ${revision} pour le versioning. Peut être défini via maven.config ou la ligne de commande (mvn verify -Drevision=4.0.1). Améliorations et corrections du Reactor Correction de bug : Gestion améliorée de --also-make lors de la reprise des builds. Nouvelle option --resume (-r) pour redémarrer à partir du dernier sous-projet en échec. Les sous-projets déjà construits avec succès sont ignorés lors de la reprise. Constructions sensibles aux sous-dossiers : Possibilité d'exécuter des outils sur des sous-projets sélectionnés uniquement. Recommandation : Utiliser mvn verify plutôt que mvn clean install. Autres Améliorations Timestamps cohérents pour tous les sous-projets dans les archives packagées. Déploiement amélioré : Le déploiement ne se produit que si tous les sous-projets sont construits avec succès. Changements de workflow, cycle de vie et exécution Java 17 requis pour exécuter Maven Java 17 est le JDK minimum requis pour exécuter Maven 4. Les anciennes versions de Java peuvent toujours être ciblées pour la compilation via Maven Toolchains. Java 17 a été préféré à Java 21 en raison d'un support à long terme plus étendu. Mise à jour des plugins et maintenance des applications Suppression des fonctionnalités obsolètes (ex. Plexus Containers, expressions ${pom.}). Mise à jour du Super POM, modifiant les versions par défaut des plugins. Les builds peuvent se comporter différemment ; définissez des versions fixes des plugins pour éviter les changements inattendus. Maven 4 affiche un avertissement si des versions par défaut sont utilisées. Nouveau paramètre “Fail on Severity” Le build peut échouer si des messages de log atteignent un niveau de gravité spécifique (ex. WARN). Utilisable via --fail-on-severity WARN ou -fos WARN. Maven Shell (mvnsh) Chaque exécution de mvn nécessitait auparavant un redémarrage complet de Java/Maven. Maven 4 introduit Maven Shell (mvnsh), qui maintient un processus Maven résident unique ouvert pour plusieurs commandes. Améliore la performance et réduit les temps de build. Alternative : Utilisez Maven Daemon (mvnd), qui gère un pool de processus Maven résidents. Architecture Un article sur les feature flags avec Unleash https://feeds.feedblitz.com//911939960/0/baeldungImplement-Feature-Flags-in-Java-With-Unleash Pour A/B testing et des cycles de développements plus rapides pour « tester en prod » Montre comment tourner sous docker unleash Et ajouter la librairie a du code java pour tester un feature flag Sécurité Keycloak 26.1 https://www.keycloak.org/2025/01/keycloak-2610-released.html detection des noeuds via la proble base de donnée aulieu echange reseau virtual threads pour infinispan et jgroups opentelemetry tracing supporté et plein de fonctionalités de sécurité Loi, société et organisation Les grands morceaux du coût et revenus d'une conférence. Ici http://bdx.io|bdx.io https://bsky.app/profile/ameliebenoit33.bsky.social/post/3lgzslhedzk2a 44% le billet 52% les sponsors 38% loc du lieu 29% traiteur et café 12% standiste 5% frais speaker (donc pas tous) Ask Me Anything Julien de Provin: J'aime beaucoup le mode “continuous testing” de Quarkus, et je me demandais s'il existait une alternative en dehors de Quarkus, ou à défaut, des ressources sur son fonctionnement ? J'aimerais beaucoup avoir un outil agnostique utilisable sur les projets non-Quarkus sur lesquels j'intervient, quitte à y metttre un peu d'huile de coude (ou de phalange pour le coup). https://github.com/infinitest/infinitest/ Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 6-7 février 2025 : Touraine Tech - Tours (France) 21 février 2025 : LyonJS 100 - Lyon (France) 28 février 2025 : Paris TS La Conf - Paris (France) 6 mars 2025 : DevCon #24 : 100% IA - Paris (France) 13 mars 2025 : Oracle CloudWorld Tour Paris - Paris (France) 14 mars 2025 : Rust In Paris 2025 - Paris (France) 19-21 mars 2025 : React Paris - Paris (France) 20 mars 2025 : PGDay Paris - Paris (France) 20-21 mars 2025 : Agile Niort - Niort (France) 25 mars 2025 : ParisTestConf - Paris (France) 26-29 mars 2025 : JChateau Unconference 2025 - Cour-Cheverny (France) 27-28 mars 2025 : SymfonyLive Paris 2025 - Paris (France) 28 mars 2025 : DataDays - Lille (France) 28-29 mars 2025 : Agile Games France 2025 - Lille (France) 3 avril 2025 : DotJS - Paris (France) 3 avril 2025 : SoCraTes Rennes 2025 - Rennes (France) 4 avril 2025 : Flutter Connection 2025 - Paris (France) 4 avril 2025 : aMP Orléans 04-04-2025 - Orléans (France) 10-11 avril 2025 : Android Makers - Montrouge (France) 10-12 avril 2025 : Devoxx Greece - Athens (Greece) 16-18 avril 2025 : Devoxx France - Paris (France) 23-25 avril 2025 : MODERN ENDPOINT MANAGEMENT EMEA SUMMIT 2025 - Paris (France) 24 avril 2025 : IA Data Day 2025 - Strasbourg (France) 29-30 avril 2025 : MixIT - Lyon (France) 7-9 mai 2025 : Devoxx UK - London (UK) 15 mai 2025 : Cloud Toulouse - Toulouse (France) 16 mai 2025 : AFUP Day 2025 Lille - Lille (France) 16 mai 2025 : AFUP Day 2025 Lyon - Lyon (France) 16 mai 2025 : AFUP Day 2025 Poitiers - Poitiers (France) 24 mai 2025 : Polycloud - Montpellier (France) 24 mai 2025 : NG Baguette Conf 2025 - Nantes (France) 5-6 juin 2025 : AlpesCraft - Grenoble (France) 5-6 juin 2025 : Devquest 2025 - Niort (France) 10-11 juin 2025 : Modern Workplace Conference Paris 2025 - Paris (France) 11-13 juin 2025 : Devoxx Poland - Krakow (Poland) 12-13 juin 2025 : Agile Tour Toulouse - Toulouse (France) 12-13 juin 2025 : DevLille - Lille (France) 13 juin 2025 : Tech F'Est 2025 - Nancy (France) 17 juin 2025 : Mobilis In Mobile - Nantes (France) 24 juin 2025 : WAX 2025 - Aix-en-Provence (France) 25-26 juin 2025 : Agi'Lille 2025 - Lille (France) 25-27 juin 2025 : BreizhCamp 2025 - Rennes (France) 26-27 juin 2025 : Sunny Tech - Montpellier (France) 1-4 juillet 2025 : Open edX Conference - 2025 - Palaiseau (France) 7-9 juillet 2025 : Riviera DEV 2025 - Sophia Antipolis (France) 18-19 septembre 2025 : API Platform Conference - Lille (France) & Online 2-3 octobre 2025 : Volcamp - Clermont-Ferrand (France) 6-10 octobre 2025 : Devoxx Belgium - Antwerp (Belgium) 9-10 octobre 2025 : Forum PHP 2025 - Marne-la-Vallée (France) 16-17 octobre 2025 : DevFest Nantes - Nantes (France) 4-7 novembre 2025 : NewCrafts 2025 - Paris (France) 6 novembre 2025 : dotAI 2025 - Paris (France) 7 novembre 2025 : BDX I/O - Bordeaux (France) 12-14 novembre 2025 : Devoxx Morocco - Marrakech (Morocco) 28-31 janvier 2026 : SnowCamp 2026 - Grenoble (France) 23-25 avril 2026 : Devoxx Greece - Athens (Greece) 17 juin 2026 : Devoxx Poland - Krakow (Poland) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

One last Gold sponsor slot is available for the AI Engineer Summit in NYC. Our last round of invites is going out soon - apply here - If you are building AI agents or AI eng teams, this will be the single highest-signal conference of the year for you!While the world melts down over DeepSeek, few are talking about the OTHER notable group of former hedge fund traders who pivoted into AI and built a remarkably profitable consumer AI business with a tiny team with incredibly cracked engineering team — Chai Research. In short order they have:* Started a Chat AI company well before Noam Shazeer started Character AI, and outlasted his departure.* Crossed 1m DAU in 2.5 years - William updates us on the pod that they've hit 1.4m DAU now, another +40% from a few months ago. Revenue crossed >$22m. * Launched the Chaiverse model crowdsourcing platform - taking 3-4 week A/B testing cycles down to 3-4 hours, and deploying >100 models a week.While they're not paying million dollar salaries, you can tell they're doing pretty well for an 11 person startup:The Chai Recipe: Building infra for rapid evalsRemember how the central thesis of LMarena (formerly LMsys) is that the only comprehensive way to evaluate LLMs is to let users try them out and pick winners?At the core of Chai is a mobile app that looks like Character AI, but is actually the largest LLM A/B testing arena in the world, specialized on retaining chat users for Chai's usecases (therapy, assistant, roleplay, etc). It's basically what LMArena would be if taken very, very seriously at one company (with $1m in prizes to boot):Chai publishes occasional research on how they think about this, including talks at their Palo Alto office:William expands upon this in today's podcast (34 mins in):Fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours.In Crowdsourcing the leap to Ten Trillion-Parameter AGI, William describes Chai's routing as a recommender system, which makes a lot more sense to us than previous pitches for model routing startups:William is notably counter-consensus in a lot of his AI product principles:* No streaming: Chats appear all at once to allow rejection sampling* No voice: Chai actually beat Character AI to introducing voice - but removed it after finding that it was far from a killer feature.* Blending: “Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model.” (that's it!)But chief above all is the recommender system.We also referenced Exa CEO Will Bryk's concept of SuperKnowlege:Full Video versionOn YouTube. please like and subscribe!Timestamps* 00:00:04 Introductions and background of William Beauchamp* 00:01:19 Origin story of Chai AI* 00:04:40 Transition from finance to AI* 00:11:36 Initial product development and idea maze for Chai* 00:16:29 User psychology and engagement with AI companions* 00:20:00 Origin of the Chai name* 00:22:01 Comparison with Character AI and funding challenges* 00:25:59 Chai's growth and user numbers* 00:34:53 Key inflection points in Chai's growth* 00:42:10 Multi-modality in AI companions and focus on user-generated content* 00:46:49 Chaiverse developer platform and model evaluation* 00:51:58 Views on AGI and the nature of AI intelligence* 00:57:14 Evaluation methods and human feedback in AI development* 01:02:01 Content creation and user experience in Chai* 01:04:49 Chai Grant program and company culture* 01:07:20 Inference optimization and compute costs* 01:09:37 Rejection sampling and reward models in AI generation* 01:11:48 Closing thoughts and recruitmentTranscriptAlessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel, and today we're in the Chai AI office with my usual co-host, Swyx.swyx [00:00:14]: Hey, thanks for having us. It's rare that we get to get out of the office, so thanks for inviting us to your home. We're in the office of Chai with William Beauchamp. Yeah, that's right. You're founder of Chai AI, but previously, I think you're concurrently also running your fund?William [00:00:29]: Yep, so I was simultaneously running an algorithmic trading company, but I fortunately was able to kind of exit from that, I think just in Q3 last year. Yeah, congrats. Yeah, thanks.swyx [00:00:43]: So Chai has always been on my radar because, well, first of all, you do a lot of advertising, I guess, in the Bay Area, so it's working. Yep. And second of all, the reason I reached out to a mutual friend, Joyce, was because I'm just generally interested in the... ...consumer AI space, chat platforms in general. I think there's a lot of inference insights that we can get from that, as well as human psychology insights, kind of a weird blend of the two. And we also share a bit of a history as former finance people crossing over. I guess we can just kind of start it off with the origin story of Chai.William [00:01:19]: Why decide working on a consumer AI platform rather than B2B SaaS? So just quickly touching on the background in finance. Sure. Originally, I'm from... I'm from the UK, born in London. And I was fortunate enough to go study economics at Cambridge. And I graduated in 2012. And at that time, everyone in the UK and everyone on my course, HFT, quant trading was really the big thing. It was like the big wave that was happening. So there was a lot of opportunity in that space. And throughout college, I'd sort of played poker. So I'd, you know, I dabbled as a professional poker player. And I was able to accumulate this sort of, you know, say $100,000 through playing poker. And at the time, as my friends would go work at companies like ChangeStreet or Citadel, I kind of did the maths. And I just thought, well, maybe if I traded my own capital, I'd probably come out ahead. I'd make more money than just going to work at ChangeStreet.swyx [00:02:20]: With 100k base as capital?William [00:02:22]: Yes, yes. That's not a lot. Well, it depends what strategies you're doing. And, you know, there is an advantage. There's an advantage to being small, right? Because there are, if you have a 10... Strategies that don't work in size. Exactly, exactly. So if you have a fund of $10 million, if you find a little anomaly in the market that you might be able to make 100k a year from, that's a 1% return on your 10 million fund. If your fund is 100k, that's 100% return, right? So being small, in some sense, was an advantage. So started off, and the, taught myself Python, and machine learning was like the big thing as well. Machine learning had really, it was the first, you know, big time machine learning was being used for image recognition, neural networks come out, you get dropout. And, you know, so this, this was the big thing that's going on at the time. So I probably spent my first three years out of Cambridge, just building neural networks, building random forests to try and predict asset prices, right, and then trade that using my own money. And that went well. And, you know, if you if you start something, and it goes well, you You try and hire more people. And the first people that came to mind was the talented people I went to college with. And so I hired some friends. And that went well and hired some more. And eventually, I kind of ran out of friends to hire. And so that was when I formed the company. And from that point on, we had our ups and we had our downs. And that was a whole long story and journey in itself. But after doing that for about eight or nine years, on my 30th birthday, which was four years ago now, I kind of took a step back to just evaluate my life, right? This is what one does when one turns 30. You know, I just heard it. I hear you. And, you know, I looked at my 20s and I loved it. It was a really special time. I was really lucky and fortunate to have worked with this amazing team, been successful, had a lot of hard times. And through the hard times, learned wisdom and then a lot of success and, you know, was able to enjoy it. And so the company was making about five million pounds a year. And it was just me and a team of, say, 15, like, Oxford and Cambridge educated mathematicians and physicists. It was like the real dream that you'd have if you wanted to start a quant trading firm. It was like...swyx [00:04:40]: Your own, all your own money?William [00:04:41]: Yeah, exactly. It was all the team's own money. We had no customers complaining to us about issues. There's no investors, you know, saying, you know, they don't like the risk that we're taking. We could. We could really run the thing exactly as we wanted it. It's like Susquehanna or like Rintec. Yeah, exactly. Yeah. And they're the companies that we would kind of look towards as we were building that thing out. But on my 30th birthday, I look and I say, OK, great. This thing is making as much money as kind of anyone would really need. And I thought, well, what's going to happen if we keep going in this direction? And it was clear that we would never have a kind of a big, big impact on the world. We can enrich ourselves. We can make really good money. Everyone on the team would be paid very, very well. Presumably, I can make enough money to buy a yacht or something. But this stuff wasn't that important to me. And so I felt a sort of obligation that if you have this much talent and if you have a talented team, especially as a founder, you want to be putting all that talent towards a good use. I looked at the time of like getting into crypto and I had a really strong view on crypto, which was that as far as a gambling device. This is like the most fun form of gambling invented in like ever super fun, I thought as a way to evade monetary regulations and banking restrictions. I think it's also absolutely amazing. So it has two like killer use cases, not so much banking the unbanked, but everything else, but everything else to do with like the blockchain and, and you know, web, was it web 3.0 or web, you know, that I, that didn't, it didn't really make much sense. And so instead of going into crypto, which I thought, even if I was successful, I'd end up in a lot of trouble. I thought maybe it'd be better to build something that governments wouldn't have a problem with. I knew that LLMs were like a thing. I think opening. I had said they hadn't released GPT-3 yet, but they'd said GPT-3 is so powerful. We can't release it to the world or something. Was it GPT-2? And then I started interacting with, I think Google had open source, some language models. They weren't necessarily LLMs, but they, but they were. But yeah, exactly. So I was able to play around with, but nowadays so many people have interacted with the chat GPT, they get it, but it's like the first time you, you can just talk to a computer and it talks back. It's kind of a special moment and you know, everyone who's done that goes like, wow, this is how it should be. Right. It should be like, rather than having to type on Google and search, you should just be able to ask Google a question. When I saw that I read the literature, I kind of came across the scaling laws and I think even four years ago. All the pieces of the puzzle were there, right? Google had done this amazing research and published, you know, a lot of it. Open AI was still open. And so they'd published a lot of their research. And so you really could be fully informed on, on the state of AI and where it was going. And so at that point I was confident enough, it was worth a shot. I think LLMs are going to be the next big thing. And so that's the thing I want to be building in, in that space. And I thought what's the most impactful product I can possibly build. And I thought it should be a platform. So I myself love platforms. I think they're fantastic because they open up an ecosystem where anyone can contribute to it. Right. So if you think of a platform like a YouTube, instead of it being like a Hollywood situation where you have to, if you want to make a TV show, you have to convince Disney to give you the money to produce it instead, anyone in the world can post any content they want to YouTube. And if people want to view it, the algorithm is going to promote it. Nowadays. You can look at creators like Mr. Beast or Joe Rogan. They would have never have had that opportunity unless it was for this platform. Other ones like Twitter's a great one, right? But I would consider Wikipedia to be a platform where instead of the Britannica encyclopedia, which is this, it's like a monolithic, you get all the, the researchers together, you get all the data together and you combine it in this, in this one monolithic source. Instead. You have this distributed thing. You can say anyone can host their content on Wikipedia. Anyone can contribute to it. And anyone can maybe their contribution is they delete stuff. When I was hearing like the kind of the Sam Altman and kind of the, the Muskian perspective of AI, it was a very kind of monolithic thing. It was all about AI is basically a single thing, which is intelligence. Yeah. Yeah. The more intelligent, the more compute, the more intelligent, and the more and better AI researchers, the more intelligent, right? They would speak about it as a kind of erased, like who can get the most data, the most compute and the most researchers. And that would end up with the most intelligent AI. But I didn't believe in any of that. I thought that's like the total, like I thought that perspective is the perspective of someone who's never actually done machine learning. Because with machine learning, first of all, you see that the performance of the models follows an S curve. So it's not like it just goes off to infinity, right? And the, the S curve, it kind of plateaus around human level performance. And you can look at all the, all the machine learning that was going on in the 2010s, everything kind of plateaued around the human level performance. And we can think about the self-driving car promises, you know, how Elon Musk kept saying the self-driving car is going to happen next year, it's going to happen next, next year. Or you can look at the image recognition, the speech recognition. You can look at. All of these things, there was almost nothing that went superhuman, except for something like AlphaGo. And we can speak about why AlphaGo was able to go like super superhuman. So I thought the most likely thing was going to be this, I thought it's not going to be a monolithic thing. That's like an encyclopedia Britannica. I thought it must be a distributed thing. And I actually liked to look at the world of finance for what I think a mature machine learning ecosystem would look like. So, yeah. So finance is a machine learning ecosystem because all of these quant trading firms are running machine learning algorithms, but they're running it on a centralized platform like a marketplace. And it's not the case that there's one giant quant trading company of all the data and all the quant researchers and all the algorithms and compute, but instead they all specialize. So one will specialize on high frequency training. Another will specialize on mid frequency. Another one will specialize on equity. Another one will specialize. And I thought that's the way the world works. That's how it is. And so there must exist a platform where a small team can produce an AI for a unique purpose. And they can iterate and build the best thing for that, right? And so that was the vision for Chai. So we wanted to build a platform for LLMs.Alessio [00:11:36]: That's kind of the maybe inside versus contrarian view that led you to start the company. Yeah. And then what was maybe the initial idea maze? Because if somebody told you that was the Hugging Face founding story, people might believe it. It's kind of like a similar ethos behind it. How did you land on the product feature today? And maybe what were some of the ideas that you discarded that initially you thought about?William [00:11:58]: So the first thing we built, it was fundamentally an API. So nowadays people would describe it as like agents, right? But anyone could write a Python script. They could submit it to an API. They could send it to the Chai backend and we would then host this code and execute it. So that's like the developer side of the platform. On their Python script, the interface was essentially text in and text out. An example would be the very first bot that I created. I think it was a Reddit news bot. And so it would first, it would pull the popular news. Then it would prompt whatever, like I just use some external API for like Burr or GPT-2 or whatever. Like it was a very, very small thing. And then the user could talk to it. So you could say to the bot, hi bot, what's the news today? And it would say, this is the top stories. And you could chat with it. Now four years later, that's like perplexity or something. That's like the, right? But back then the models were first of all, like really, really dumb. You know, they had an IQ of like a four year old. And users, there really wasn't any demand or any PMF for interacting with the news. So then I was like, okay. Um. So let's make another one. And I made a bot, which was like, you could talk to it about a recipe. So you could say, I'm making eggs. Like I've got eggs in my fridge. What should I cook? And it'll say, you should make an omelet. Right. There was no PMF for that. No one used it. And so I just kept creating bots. And so every single night after work, I'd be like, okay, I like, we have AI, we have this platform. I can create any text in textile sort of agent and put it on the platform. And so we just create stuff night after night. And then all the coders I knew, I would say, yeah, this is what we're going to do. And then I would say to them, look, there's this platform. You can create any like chat AI. You should put it on. And you know, everyone's like, well, chatbots are super lame. We want absolutely nothing to do with your chatbot app. No one who knew Python wanted to build on it. I'm like trying to build all these bots and no consumers want to talk to any of them. And then my sister who at the time was like just finishing college or something, I said to her, I was like, if you want to learn Python, you should just submit a bot for my platform. And she, she built a therapy for me. And I was like, okay, cool. I'm going to build a therapist bot. And then the next day I checked the performance of the app and I'm like, oh my God, we've got 20 active users. And they spent, they spent like an average of 20 minutes on the app. I was like, oh my God, what, what bot were they speaking to for an average of 20 minutes? And I looked and it was the therapist bot. And I went, oh, this is where the PMF is. There was no demand for, for recipe help. There was no demand for news. There was no demand for dad jokes or pub quiz or fun facts or what they wanted was they wanted the therapist bot. the time I kind of reflected on that and I thought, well, if I want to consume news, the most fun thing, most fun way to consume news is like Twitter. It's not like the value of there being a back and forth, wasn't that high. Right. And I thought if I need help with a recipe, I actually just go like the New York times has a good recipe section, right? It's not actually that hard. And so I just thought the thing that AI is 10 X better at is a sort of a conversation right. That's not intrinsically informative, but it's more about an opportunity. You can say whatever you want. You're not going to get judged. If it's 3am, you don't have to wait for your friend to text back. It's like, it's immediate. They're going to reply immediately. You can say whatever you want. It's judgment-free and it's much more like a playground. It's much more like a fun experience. And you could see that if the AI gave a person a compliment, they would love it. It's much easier to get the AI to give you a compliment than a human. From that day on, I said, okay, I get it. Humans want to speak to like humans or human like entities and they want to have fun. And that was when I started to look less at platforms like Google. And I started to look more at platforms like Instagram. And I was trying to think about why do people use Instagram? And I could see that I think Chai was, was filling the same desire or the same drive. If you go on Instagram, typically you want to look at the faces of other humans, or you want to hear about other people's lives. So if it's like the rock is making himself pancakes on a cheese plate. You kind of feel a little bit like you're the rock's friend, or you're like having pancakes with him or something, right? But if you do it too much, you feel like you're sad and like a lonely person, but with AI, you can talk to it and tell it stories and tell you stories, and you can play with it for as long as you want. And you don't feel like you're like a sad, lonely person. You feel like you actually have a friend.Alessio [00:16:29]: And what, why is that? Do you have any insight on that from using it?William [00:16:33]: I think it's just the human psychology. I think it's just the idea that, with old school social media. You're just consuming passively, right? So you'll just swipe. If I'm watching TikTok, just like swipe and swipe and swipe. And even though I'm getting the dopamine of like watching an engaging video, there's this other thing that's building my head, which is like, I'm feeling lazier and lazier and lazier. And after a certain period of time, I'm like, man, I just wasted 40 minutes. I achieved nothing. But with AI, because you're interacting, you feel like you're, it's not like work, but you feel like you're participating and contributing to the thing. You don't feel like you're just. Consuming. So you don't have a sense of remorse basically. And you know, I think on the whole people, the way people talk about, try and interact with the AI, they speak about it in an incredibly positive sense. Like we get people who say they have eating disorders saying that the AI helps them with their eating disorders. People who say they're depressed, it helps them through like the rough patches. So I think there's something intrinsically healthy about interacting that TikTok and Instagram and YouTube doesn't quite tick. From that point on, it was about building more and more kind of like human centric AI for people to interact with. And I was like, okay, let's make a Kanye West bot, right? And then no one wanted to talk to the Kanye West bot. And I was like, ah, who's like a cool persona for teenagers to want to interact with. And I was like, I was trying to find the influencers and stuff like that, but no one cared. Like they didn't want to interact with the, yeah. And instead it was really just the special moment was when we said the realization that developers and software engineers aren't interested in building this sort of AI, but the consumers are right. And rather than me trying to guess every day, like what's the right bot to submit to the platform, why don't we just create the tools for the users to build it themselves? And so nowadays this is like the most obvious thing in the world, but when Chai first did it, it was not an obvious thing at all. Right. Right. So we took the API for let's just say it was, I think it was GPTJ, which was this 6 billion parameter open source transformer style LLM. We took GPTJ. We let users create the prompt. We let users select the image and we let users choose the name. And then that was the bot. And through that, they could shape the experience, right? So if they said this bot's going to be really mean, and it's going to be called like bully in the playground, right? That was like a whole category that I never would have guessed. Right. People love to fight. They love to have a disagreement, right? And then they would create, there'd be all these romantic archetypes that I didn't know existed. And so as the users could create the content that they wanted, that was when Chai was able to, to get this huge variety of content and rather than appealing to, you know, 1% of the population that I'd figured out what they wanted, you could appeal to a much, much broader thing. And so from that moment on, it was very, very crystal clear. It's like Chai, just as Instagram is this social media platform that lets people create images and upload images, videos and upload that, Chai was really about how can we let the users create this experience in AI and then share it and interact and search. So it's really, you know, I say it's like a platform for social AI.Alessio [00:20:00]: Where did the Chai name come from? Because you started the same path. I was like, is it character AI shortened? You started at the same time, so I was curious. The UK origin was like the second, the Chai.William [00:20:15]: We started way before character AI. And there's an interesting story that Chai's numbers were very, very strong, right? So I think in even 20, I think late 2022, was it late 2022 or maybe early 2023? Chai was like the number one AI app in the app store. So we would have something like 100,000 daily active users. And then one day we kind of saw there was this website. And we were like, oh, this website looks just like Chai. And it was the character AI website. And I think that nowadays it's, I think it's much more common knowledge that when they left Google with the funding, I think they knew what was the most trending, the number one app. And I think they sort of built that. Oh, you found the people.swyx [00:21:03]: You found the PMF for them.William [00:21:04]: We found the PMF for them. Exactly. Yeah. So I worked a year very, very hard. And then they, and then that was when I learned a lesson, which is that if you're VC backed and if, you know, so Chai, we'd kind of ran, we'd got to this point, I was the only person who'd invested. I'd invested maybe 2 million pounds in the business. And you know, from that, we were able to build this thing, get to say a hundred thousand daily active users. And then when character AI came along, the first version, we sort of laughed. We were like, oh man, this thing sucks. Like they don't know what they're building. They're building the wrong thing anyway, but then I saw, oh, they've raised a hundred million dollars. Oh, they've raised another hundred million dollars. And then our users started saying, oh guys, your AI sucks. Cause we were serving a 6 billion parameter model, right? How big was the model that character AI could afford to serve, right? So we would be spending, let's say we would spend a dollar per per user, right? Over the, the, you know, the entire lifetime.swyx [00:22:01]: A dollar per session, per chat, per month? No, no, no, no.William [00:22:04]: Let's say we'd get over the course of the year, we'd have a million users and we'd spend a million dollars on the AI throughout the year. Right. Like aggregated. Exactly. Exactly. Right. They could spend a hundred times that. So people would say, why is your AI much dumber than character AIs? And then I was like, oh, okay, I get it. This is like the Silicon Valley style, um, hyper scale business. And so, yeah, we moved to Silicon Valley and, uh, got some funding and iterated and built the flywheels. And, um, yeah, I, I'm very proud that we were able to compete with that. Right. So, and I think the reason we were able to do it was just customer obsession. And it's similar, I guess, to how deep seek have been able to produce such a compelling model when compared to someone like an open AI, right? So deep seek, you know, their latest, um, V2, yeah, they claim to have spent 5 million training it.swyx [00:22:57]: It may be a bit more, but, um, like, why are you making it? Why are you making such a big deal out of this? Yeah. There's an agenda there. Yeah. You brought up deep seek. So we have to ask you had a call with them.William [00:23:07]: We did. We did. We did. Um, let me think what to say about that. I think for one, they have an amazing story, right? So their background is again in finance.swyx [00:23:16]: They're the Chinese version of you. Exactly.William [00:23:18]: Well, there's a lot of similarities. Yes. Yes. I have a great affinity for companies which are like, um, founder led, customer obsessed and just try and build something great. And I think what deep seek have achieved. There's quite special is they've got this amazing inference engine. They've been able to reduce the size of the KV cash significantly. And then by being able to do that, they're able to significantly reduce their inference costs. And I think with kind of with AI, people get really focused on like the kind of the foundation model or like the model itself. And they sort of don't pay much attention to the inference. To give you an example with Chai, let's say a typical user session is 90 minutes, which is like, you know, is very, very long for comparison. Let's say the average session length on TikTok is 70 minutes. So people are spending a lot of time. And in that time they're able to send say 150 messages. That's a lot of completions, right? It's quite different from an open AI scenario where people might come in, they'll have a particular question in mind. And they'll ask like one question. And a few follow up questions, right? So because they're consuming, say 30 times as many requests for a chat, or a conversational experience, you've got to figure out how to how to get the right balance between the cost of that and the quality. And so, you know, I think with AI, it's always been the case that if you want a better experience, you can throw compute at the problem, right? So if you want a better model, you can just make it bigger. If you want it to remember better, give it a longer context. And now, what open AI is doing to great fanfare is with projection sampling, you can generate many candidates, right? And then with some sort of reward model or some sort of scoring system, you can serve the most promising of these many candidates. And so that's kind of scaling up on the inference time compute side of things. And so for us, it doesn't make sense to think of AI is just the absolute performance. So. But what we're seeing, it's like the MML you score or the, you know, any of these benchmarks that people like to look at, if you just get that score, it doesn't really tell tell you anything. Because it's really like progress is made by improving the performance per dollar. And so I think that's an area where deep seek have been able to form very, very well, surprisingly so. And so I'm very interested in what Lama four is going to look like. And if they're able to sort of match what deep seek have been able to achieve with this performance per dollar gain.Alessio [00:25:59]: Before we go into the inference, some of the deeper stuff, can you give people an overview of like some of the numbers? So I think last I checked, you have like 1.4 million daily active now. It's like over 22 million of revenue. So it's quite a business.William [00:26:12]: Yeah, I think we grew by a factor of, you know, users grew by a factor of three last year. Revenue over doubled. You know, it's very exciting. We're competing with some really big, really well funded companies. Character AI got this, I think it was almost a $3 billion valuation. And they have 5 million DAU is a number that I last heard. Torquay, which is a Chinese built app owned by a company called Minimax. They're incredibly well funded. And these companies didn't grow by a factor of three last year. Right. And so when you've got this company and this team that's able to keep building something that gets users excited, and they want to tell their friend about it, and then they want to come and they want to stick on the platform. I think that's very special. And so last year was a great year for the team. And yeah, I think the numbers reflect the hard work that we put in. And then fundamentally, the quality of the app, the quality of the content, the quality of the content, the quality of the content, the quality of the content, the quality of the content. AI is the quality of the experience that you have. You actually published your DAU growth chart, which is unusual. And I see some inflections. Like, it's not just a straight line. There's some things that actually inflect. Yes. What were the big ones? Cool. That's a great, great, great question. Let me think of a good answer. I'm basically looking to annotate this chart, which doesn't have annotations on it. Cool. The first thing I would say is this is, I think the most important thing to know about success is that success is born out of failures. Right? Through failures that we learn. You know, if you think something's a good idea, and you do and it works, great, but you didn't actually learn anything, because everything went exactly as you imagined. But if you have an idea, you think it's going to be good, you try it, and it fails. There's a gap between the reality and expectation. And that's an opportunity to learn. The flat periods, that's us learning. And then the up periods is that's us reaping the rewards of that. So I think the big, of the growth shot of just 2024, I think the first thing that really kind of put a dent in our growth was our backend. So we just reached this scale. So we'd, from day one, we'd built on top of Google's GCP, which is Google's cloud platform. And they were fantastic. We used them when we had one daily active user, and they worked pretty good all the way up till we had about 500,000. It was never the cheapest, but from an engineering perspective, man, that thing scaled insanely good. Like, not Vertex? Not Vertex. Like GKE, that kind of stuff? We use Firebase. So we use Firebase. I'm pretty sure we're the biggest user ever on Firebase. That's expensive. Yeah, we had calls with engineers, and they're like, we wouldn't recommend using this product beyond this point, and you're 3x over that. So we pushed Google to their absolute limits. You know, it was fantastic for us, because we could focus on the AI. We could focus on just adding as much value as possible. But then what happened was, after 500,000, just the thing, the way we were using it, and it would just, it wouldn't scale any further. And so we had a really, really painful, at least three-month period, as we kind of migrated between different services, figuring out, like, what requests do we want to keep on Firebase, and what ones do we want to move on to something else? And then, you know, making mistakes. And learning things the hard way. And then after about three months, we got that right. So that, we would then be able to scale to the 1.5 million DAE without any further issues from the GCP. But what happens is, if you have an outage, new users who go on your app experience a dysfunctional app, and then they're going to exit. And so your next day, the key metrics that the app stores track are going to be something like retention rates. And so your next day, the key metrics that the app stores track are going to be something like retention rates. Money spent, and the star, like, the rating that they give you. In the app store. In the app store, yeah. Tyranny. So if you're ranked top 50 in entertainment, you're going to acquire a certain rate of users organically. If you go in and have a bad experience, it's going to tank where you're positioned in the algorithm. And then it can take a long time to kind of earn your way back up, at least if you wanted to do it organically. If you throw money at it, you can jump to the top. And I could talk about that. But broadly speaking, if we look at 2024, the first kink in the graph was outages due to hitting 500k DAU. The backend didn't want to scale past that. So then we just had to do the engineering and build through it. Okay, so we built through that, and then we get a little bit of growth. And so, okay, that's feeling a little bit good. I think the next thing, I think it's, I'm not going to lie, I have a feeling that when Character AI got... I was thinking. I think so. I think... So the Character AI team fundamentally got acquired by Google. And I don't know what they changed in their business. I don't know if they dialed down that ad spend. Products don't change, right? Products just what it is. I don't think so. Yeah, I think the product is what it is. It's like maintenance mode. Yes. I think the issue that people, you know, some people may think this is an obvious fact, but running a business can be very competitive, right? Because other businesses can see what you're doing, and they can imitate you. And then there's this... There's this question of, if you've got one company that's spending $100,000 a day on advertising, and you've got another company that's spending zero, if you consider market share, and if you're considering new users which are entering the market, the guy that's spending $100,000 a day is going to be getting 90% of those new users. And so I have a suspicion that when the founders of Character AI left, they dialed down their spending on user acquisition. And I think that kind of gave oxygen to like the other apps. And so Chai was able to then start growing again in a really healthy fashion. I think that's kind of like the second thing. I think a third thing is we've really built a great data flywheel. Like the AI team sort of perfected their flywheel, I would say, in end of Q2. And I could speak about that at length. But fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours. And when we did that, we could really, really, really perfect techniques like DPO, fine tuning, prompt engineering, blending, rejection sampling, training a reward model, right, really successfully, like boom, boom, boom, boom, boom. And so I think in Q3 and Q4, we got, the amount of AI improvements we got was like astounding. It was getting to the point, I thought like how much more, how much more edge is there to be had here? But the team just could keep going and going and going. That was like number three for the inflection point.swyx [00:34:53]: There's a fourth?William [00:34:54]: The important thing about the third one is if you go on our Reddit or you talk to users of AI, there's like a clear date. It's like somewhere in October or something. The users, they flipped. Before October, the users... The users would say character AI is better than you, for the most part. Then from October onwards, they would say, wow, you guys are better than character AI. And that was like a really clear positive signal that we'd sort of done it. And I think people, you can't cheat consumers. You can't trick them. You can't b******t them. They know, right? If you're going to spend 90 minutes on a platform, and with apps, there's the barriers to switching is pretty low. Like you can try character AI, you can't cheat consumers. You can't cheat them. You can't cheat them. You can't cheat AI for a day. If you get bored, you can try Chai. If you get bored of Chai, you can go back to character. So the users, the loyalty is not strong, right? What keeps them on the app is the experience. If you deliver a better experience, they're going to stay and they can tell. So that was the fourth one was we were fortunate enough to get this hire. He was hired one really talented engineer. And then they said, oh, at my last company, we had a head of growth. He was really, really good. And he was the head of growth for ByteDance for two years. Would you like to speak to him? And I was like, yes. Yes, I think I would. And so I spoke to him. And he just blew me away with what he knew about user acquisition. You know, it was like a 3D chessswyx [00:36:21]: sort of thing. You know, as much as, as I know about AI. Like ByteDance as in TikTok US. Yes.William [00:36:26]: Not ByteDance as other stuff. Yep. He was interviewing us as we were interviewing him. Right. And so pick up options. Yeah, exactly. And so he was kind of looking at our metrics. And he was like, I saw him get really excited when he said, guys, you've got a million daily active users and you've done no advertising. I said, correct. And he was like, that's unheard of. He's like, I've never heard of anyone doing that. And then he started looking at our metrics. And he was like, if you've got all of this organically, if you start spending money, this is going to be very exciting. I was like, let's give it a go. So then he came in, we've just started ramping up the user acquisition. So that looks like spending, you know, let's say we're spending, we started spending $20,000 a day, it looked very promising than 20,000. Right now we're spending $40,000 a day on user acquisition. That's still only half of what like character AI or talkie may be spending. But from that, it's sort of, we were growing at a rate of maybe say, 2x a year. And that got us growing at a rate of 3x a year. So I'm growing, I'm evolving more and more to like a Silicon Valley style hyper growth, like, you know, you build something decent, and then you canswyx [00:37:33]: slap on a huge... You did the important thing, you did the product first.William [00:37:36]: Of course, but then you can slap on like, like the rocket or the jet engine or something, which is just this cash in, you pour in as much cash, you buy a lot of ads, and your growth is faster.swyx [00:37:48]: Not to, you know, I'm just kind of curious what's working right now versus what surprisinglyWilliam [00:37:52]: doesn't work. Oh, there's a long, long list of surprising stuff that doesn't work. Yeah. The surprising thing, like the most surprising thing, what doesn't work is almost everything doesn't work. That's what's surprising. And I'll give you an example. So like a year and a half ago, I was working at a company, we were super excited by audio. I was like, audio is going to be the next killer feature, we have to get in the app. And I want to be the first. So everything Chai does, I want us to be the first. We may not be the company that's strongest at execution, but we can always be theswyx [00:38:22]: most innovative. Interesting. Right? So we can... You're pretty strong at execution.William [00:38:26]: We're much stronger, we're much stronger. A lot of the reason we're here is because we were first. If we launched today, it'd be so hard to get the traction. Because it's like to get the flywheel, to get the users, to build a product people are excited about. If you're first, people are naturally excited about it. But if you're fifth or 10th, man, you've got to beswyx [00:38:46]: insanely good at execution. So you were first with voice? We were first. We were first. I only knowWilliam [00:38:51]: when character launched voice. They launched it, I think they launched it at least nine months after us. Okay. Okay. But the team worked so hard for it. At the time we did it, latency is a huge problem. Cost is a huge problem. Getting the right quality of the voice is a huge problem. Right? Then there's this user interface and getting the right user experience. Because you don't just want it to start blurting out. Right? You want to kind of activate it. But then you don't have to keep pressing a button every single time. There's a lot that goes into getting a really smooth audio experience. So we went ahead, we invested the three months, we built it all. And then when we did the A-B test, there was like, no change in any of the numbers. And I was like, this can't be right, there must be a bug. And we spent like a week just checking everything, checking again, checking again. And it was like, the users just did not care. And it was something like only 10 or 15% of users even click the button to like, they wanted to engage the audio. And they would only use it for 10 or 15% of the time. So if you do the math, if it's just like something that one in seven people use it for one seventh of their time. You've changed like 2% of the experience. So even if that that 2% of the time is like insanely good, it doesn't translate much when you look at the retention, when you look at the engagement, and when you look at the monetization rates. So audio did not have a big impact. I'm pretty big on audio. But yeah, I like it too. But it's, you know, so a lot of the stuff which I do, I'm a big, you can have a theory. And you resist. Yeah. Exactly, exactly. So I think if you want to make audio work, it has to be a unique, compelling, exciting experience that they can't have anywhere else.swyx [00:40:37]: It could be your models, which just weren't good enough.William [00:40:39]: No, no, no, they were great. Oh, yeah, they were very good. it was like, it was kind of like just the, you know, if you listen to like an audible or Kindle, or something like, you just hear this voice. And it's like, you don't go like, wow, this is this is special, right? It's like a convenience thing. But the idea is that if you can, if Chai is the only platform, like, let's say you have a Mr. Beast, and YouTube is the only platform you can use to make audio work, then you can watch a Mr. Beast video. And it's the most engaging, fun video that you want to watch, you'll go to a YouTube. And so it's like for audio, you can't just put the audio on there. And people go, oh, yeah, it's like 2% better. Or like, 5% of users think it's 20% better, right? It has to be something that the majority of people, for the majority of the experience, go like, wow, this is a big deal. That's the features you need to be shipping. If it's not going to appeal to the majority of people, for the majority of the experience, and it's not a big deal, it's not going to move you. Cool. So you killed it. I don't see it anymore. Yep. So I love this. The longer, it's kind of cheesy, I guess, but the longer I've been working at Chai, and I think the team agrees with this, all the platitudes, at least I thought they were platitudes, that you would get from like the Steve Jobs, which is like, build something insanely great, right? Or be maniacally focused, or, you know, the most important thing is saying no to, not to work on. All of these sort of lessons, they just are like painfully true. They're painfully true. So now I'm just like, everything I say, I'm either quoting Steve Jobs or Zuckerberg. I'm like, guys, move fast and break free.swyx [00:42:10]: You've jumped the Apollo to cool it now.William [00:42:12]: Yeah, it's just so, everything they said is so, so true. The turtle neck. Yeah, yeah, yeah. Everything is so true.swyx [00:42:18]: This last question on my side, and I want to pass this to Alessio, is on just, just multi-modality in general. This actually comes from Justine Moore from A16Z, who's a friend of ours. And a lot of people are trying to do voice image video for AI companions. Yes. You just said voice didn't work. Yep. What would make you revisit?William [00:42:36]: So Steve Jobs, he was very, listen, he was very, very clear on this. There's a habit of engineers who, once they've got some cool technology, they want to find a way to package up the cool technology and sell it to consumers, right? That does not work. So you're free to try and build a startup where you've got your cool tech and you want to find someone to sell it to. That's not what we do at Chai. At Chai, we start with the consumer. What does the consumer want? What is their problem? And how do we solve it? So right now, the number one problems for the users, it's not the audio. That's not the number one problem. It's not the image generation either. That's not their problem either. The number one problem for users in AI is this. All the AI is being generated by middle-aged men in Silicon Valley, right? That's all the content. You're interacting with this AI. You're speaking to it for 90 minutes on average. It's being trained by middle-aged men. The guys out there, they're out there. They're talking to you. They're talking to you. They're like, oh, what should the AI say in this situation, right? What's funny, right? What's cool? What's boring? What's entertaining? That's not the way it should be. The way it should be is that the users should be creating the AI, right? And so the way I speak about it is this. Chai, we have this AI engine in which sits atop a thin layer of UGC. So the thin layer of UGC is absolutely essential, right? It's just prompts. But it's just prompts. It's just an image. It's just a name. It's like we've done 1% of what we could do. So we need to keep thickening up that layer of UGC. It must be the case that the users can train the AI. And if reinforcement learning is powerful and important, they have to be able to do that. And so it's got to be the case that there exists, you know, I say to the team, just as Mr. Beast is able to spend 100 million a year or whatever it is on his production company, and he's got a team building the content, the Mr. Beast company is able to spend 100 million a year on his production company. And he's got a team building the content, which then he shares on the YouTube platform. Until there's a team that's earning 100 million a year or spending 100 million on the content that they're producing for the Chai platform, we're not finished, right? So that's the problem. That's what we're excited to build. And getting too caught up in the tech, I think is a fool's errand. It does not work.Alessio [00:44:52]: As an aside, I saw the Beast Games thing on Amazon Prime. It's not doing well. And I'mswyx [00:44:56]: curious. It's kind of like, I mean, the audience reading is high. The run-to-meet-all sucks, but the audience reading is high.Alessio [00:45:02]: But it's not like in the top 10. I saw it dropped off of like the... Oh, okay. Yeah, that one I don't know. I'm curious, like, you know, it's kind of like similar content, but different platform. And then going back to like, some of what you were saying is like, you know, people come to ChaiWilliam [00:45:13]: expecting some type of content. Yeah, I think it's something that's interesting to discuss is like, is moats. And what is the moat? And so, you know, if you look at a platform like YouTube, the moat, I think is in first is really is in the ecosystem. And the ecosystem, is comprised of you have the content creators, you have the users, the consumers, and then you have the algorithms. And so this, this creates a sort of a flywheel where the algorithms are able to be trained on the users, and the users data, the recommend systems can then feed information to the content creators. So Mr. Beast, he knows which thumbnail does the best. He knows the first 10 seconds of the video has to be this particular way. And so his content is super optimized for the YouTube platform. So that's why it doesn't do well on Amazon. If he wants to do well on Amazon, how many videos has he created on the YouTube platform? By thousands, 10s of 1000s, I guess, he needs to get those iterations in on the Amazon. So at Chai, I think it's all about how can we get the most compelling, rich user generated content, stick that on top of the AI engine, the recommender systems, in such that we get this beautiful data flywheel, more users, better recommendations, more creative, more content, more users.Alessio [00:46:34]: You mentioned the algorithm, you have this idea of the Chaiverse on Chai, and you have your own kind of like LMSYS-like ELO system. Yeah, what are things that your models optimize for, like your users optimize for, and maybe talk about how you build it, how people submit models?William [00:46:49]: So Chaiverse is what I would describe as a developer platform. More often when we're speaking about Chai, we're thinking about the Chai app. And the Chai app is really this product for consumers. And so consumers can come on the Chai app, they can come on the Chai app, they can come on the Chai app, they can interact with our AI, and they can interact with other UGC. And it's really just these kind of bots. And it's a thin layer of UGC. Okay. Our mission is not to just have a very thin layer of UGC. Our mission is to have as much UGC as possible. So we must have, I don't want people at Chai training the AI. I want people, not middle aged men, building AI. I want everyone building the AI, as many people building the AI as possible. Okay, so what we built was we built Chaiverse. And Chaiverse is kind of, it's kind of like a prototype, is the way to think about it. And it started with this, this observation that, well, how many models get submitted into Hugging Face a day? It's hundreds, it's hundreds, right? So there's hundreds of LLMs submitted each day. Now consider that, what does it take to build an LLM? It takes a lot of work, actually. It's like someone devoted several hours of compute, several hours of their time, prepared a data set, launched it, ran it, evaluated it, submitted it, right? So there's a lot of, there's a lot of, there's a lot of work that's going into that. So what we did was we said, well, why can't we host their models for them and serve them to users? And then what would that look like? The first issue is, well, how do you know if a model is good or not? Like, we don't want to serve users the crappy models, right? So what we would do is we would, I love the LMSYS style. I think it's really cool. It's really simple. It's a very intuitive thing, which is you simply present the users with two completions. You can say, look, this is from model one. This is from model two. This is from model three. This is from model A. This is from model B, which is better. And so if someone submits a model to Chaiverse, what we do is we spin up a GPU. We download the model. We're going to now host that model on this GPU. And we're going to start routing traffic to it. And we're going to send, we think it takes about 5,000 completions to get an accurate signal. That's roughly what LMSYS does. And from that, we're able to get an accurate ranking. And we're able to get an accurate ranking. And we're able to get an accurate ranking of which models are people finding entertaining and which models are not entertaining. If you look at the bottom 80%, they'll suck. You can just disregard them. They totally suck. Then when you get the top 20%, you know you've got a decent model, but you can break it down into more nuance. There might be one that's really descriptive. There might be one that's got a lot of personality to it. There might be one that's really illogical. Then the question is, well, what do you do with these top models? From that, you can do more sophisticated things. You can try and do like a routing thing where you say for a given user request, we're going to try and predict which of these end models that users enjoy the most. That turns out to be pretty expensive and not a huge source of like edge or improvement. Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model. Just a random 50%? Just a random, yeah. And then... That's blending? That's blending. You can do more sophisticated things on top of that, as in all things in life, but the 80-20 solution, if you just do that, you get a pretty powerful effect out of the gate. Random number generator. I think it's like the robustness of randomness. Random is a very powerful optimization technique, and it's a very robust thing. So you can explore a lot of the space very efficiently. There's one thing that's really, really important to share, and this is the most exciting thing for me, is after you do the ranking, you get an ELO score, and you can track a user's first join date, the first date they submit a model to Chaiverse, they almost always get a terrible ELO, right? So let's say the first submission they get an ELO of 1,100 or 1,000 or something, and you can see that they iterate and they iterate and iterate, and it will be like, no improvement, no improvement, no improvement, and then boom. Do you give them any data, or do you have to come up with this themselves? We do, we do, we do, we do. We try and strike a balance between giving them data that's very useful, you've got to be compliant with GDPR, which is like, you have to work very hard to preserve the privacy of users of your app. So we try to give them as much signal as possible, to be helpful. The minimum is we're just going to give you a score, right? That's the minimum. But that alone is people can optimize a score pretty well, because they're able to come up with theories, submit it, does it work? No. A new theory, does it work? No. And then boom, as soon as they figure something out, they keep it, and then they iterate, and then boom,Alessio [00:51:46]: they figure something out, and they keep it. Last year, you had this post on your blog, cross-sourcing the lead to the 10 trillion parameter, AGI, and you call it a mixture of experts, recommenders. Yep. Any insights?William [00:51:58]: Updated thoughts, 12 months later? I think the odds, the timeline for AGI has certainly been pushed out, right? Now, this is in, I'm a controversial person, I don't know, like, I just think... You don't believe in scaling laws, you think AGI is further away. I think it's an S-curve. I think everything's an S-curve. And I think that the models have proven to just be far worse at reasoning than people sort of thought. And I think whenever I hear people talk about LLMs as reasoning engines, I sort of cringe a bit. I don't think that's what they are. I think of them more as like a simulator. I think of them as like a, right? So they get trained to predict the next most likely token. It's like a physics simulation engine. So you get these like games where you can like construct a bridge, and you drop a car down, and then it predicts what should happen. And that's really what LLMs are doing. It's not so much that they're reasoning, it's more that they're just doing the most likely thing. So fundamentally, the ability for people to add in intelligence, I think is very limited. What most people would consider intelligence, I think the AI is not a crowdsourcing problem, right? Now with Wikipedia, Wikipedia crowdsources knowledge. It doesn't crowdsource intelligence. So it's a subtle distinction. AI is fantastic at knowledge. I think it's weak at intelligence. And a lot, it's easy to conflate the two because if you ask it a question and it gives you, you know, if you said, who was the seventh president of the United States, and it gives you the correct answer, I'd say, well, I don't know the answer to that. And you can conflate that with intelligence. But really, that's a question of knowledge. And knowledge is really this thing about saying, how can I store all of this information? And then how can I retrieve something that's relevant? Okay, they're fantastic at that. They're fantastic at storing knowledge and retrieving the relevant knowledge. They're superior to humans in that regard. And so I think we need to come up for a new word. How does one describe AI should contain more knowledge than any individual human? It should be more accessible than any individual human. That's a very powerful thing. That's superswyx [00:54:07]: powerful. But what words do we use to describe that? We had a previous guest on Exa AI that does search. And he tried to coin super knowledge as the opposite of super intelligence.William [00:54:20]: Exactly. I think super knowledge is a more accurate word for it.swyx [00:54:24]: You can store more things than any human can.William [00:54:26]: And you can retrieve it better than any human can as well. And I think it's those two things combined that's special. I think that thing will exist. That thing can be built. And I think you can start with something that's entertaining and fun. And I think, I often think it's like, look, it's going to be a 20 year journey. And we're in like, year four, or it's like the web. And this is like 1998 or something. You know, you've got a long, long way to go before the Amazon.coms are like these huge, multi trillion dollar businesses that every single person uses every day. And so AI today is very simplistic. And it's fundamentally the way we're using it, the flywheels, and this ability for how can everyone contribute to it to really magnify the value that it brings. Right now, like, I think it's a bit sad. It's like, right now you have big labs, I'm going to pick on open AI. And they kind of go to like these human labelers. And they say, we're going to pay you to just label this like subset of questions that we want to get a really high quality data set, then we're going to get like our own computers that are really powerful. And that's kind of like the thing. For me, it's so much like Encyclopedia Britannica. It's like insane. All the people that were interested in blockchain, it's like, well, this is this is what needs to be decentralized, you need to decentralize that thing. Because if you distribute it, people can generate way more data in a distributed fashion, way more, right? You need the incentive. Yeah, of course. Yeah. But I mean, the, the, that's kind of the exciting thing about Wikipedia was it's this understanding, like the incentives, you don't need money to incentivize people. You don't need dog coins. No. Sometimes, sometimes people get the satisfaction fro

Danny Bernstein is the Managing Partner of Reservoir Ventures and the CEO of the Reservoir, an ecosystem of nonprofit and for-profit ventures tackling California's most urgent challenges and opportunities. Before founding the Reservoir, Danny spent 20 years in Silicon Valley leading business development, partnerships, and developer programs at Google and Microsoft. At Google, he worked across products like Search, Chrome, Firebase, and Google Identity after the acquisition of Meebo, a Web 2.0 startup that was sold to Google 2012. At Microsoft, he led critical product lines for Microsoft Teams. — This episode is presented by MyLand. Learn more HERE. — Links The Reservoir - https://www.reservoir.co Danny on Linkedin - https://www.linkedin.com/in/dannybernstein/ Join the Co-op - https://themodernacre.supercast.com Subscribe to the Newsletter - https://themodernacre.substack.com

The full schedule for Latent Space LIVE! at NeurIPS has been announced, featuring Best of 2024 overview talks for the AI Startup Landscape, Computer Vision, Open Models, Transformers Killers, Synthetic Data, Agents, and Scaling, and speakers from Sarah Guo of Conviction, Roboflow, AI2/Meta, Recursal/Together, HuggingFace, OpenHands and SemiAnalysis. Join us for the IRL event/Livestream! Alessio will also be holding a meetup at AWS Re:Invent in Las Vegas this Wednesday. See our new Events page for dates of AI Engineer Summit, Singapore, and World's Fair in 2025. LAST CALL for questions for our big 2024 recap episode! Submit questions and messages on Speakpipe here for a chance to appear on the show!When we first observed that GPT Wrappers are Good, Actually, we did not even have Bolt on our radar. Since we recorded our Anthropic episode discussing building Agents with the new Claude 3.5 Sonnet, Bolt.new (by Stackblitz) has easily cleared the $8m ARR bar, repeating and accelerating its initial $4m feat.There are very many AI code generators and VS Code forks out there, but Bolt probably broke through initially because of its incredible zero shot low effort app generation:But as we explain in the pod, Bolt also emphasized deploy (Netlify)/ backend (Supabase)/ fullstack capabilities on top of Stackblitz's existing WebContainer full-WASM-powered-developer-environment-in-the-browser tech. Since then, the team has been shipping like mad (with weekly office hours), with bugfixing, full screen, multi-device, long context, diff based edits (using speculative decoding like we covered in Inference, Fast and Slow).All of this has captured the imagination of low/no code builders like Greg Isenberg and many others on YouTube/TikTok/Reddit/X/Linkedin etc:Just as with Fireworks, our relationship with Bolt/Stackblitz goes a bit deeper than normal - swyx advised the launch and got a front row seat to this epic journey, as well as demoed it with Realtime Voice at the recent OpenAI Dev Day. So we are very proud to be the first/closest to tell the full open story of Bolt/Stackblitz!Flow Engineering + Qodo/AlphaCodium UpdateIn year 2 of the pod we have been on a roll getting former guests to return as guest cohosts (Harrison Chase, Aman Sanger, Jon Frankle), and it was a pleasure to catch Itamar Friedman back on the pod, giving us an update on all things Qodo and Testing Agents from our last catchup a year and a half ago:Qodo (they renamed in September) went viral in early January this year with AlphaCodium (paper here, code here) beating DeepMind's AlphaCode with high efficiency:With a simple problem solving code agent:* The first step is to have the model reason about the problem. They describe it using bullet points and focus on the goal, inputs, outputs, rules, constraints, and any other relevant details.* Then, they make the model reason about the public tests and come up with an explanation of why the input leads to that particular output. * The model generates two to three potential solutions in text and ranks them in terms of correctness, simplicity, and robustness. * Then, it generates more diverse tests for the problem, covering cases not part of the original public tests. * Iteratively, pick a solution, generate the code, and run it on a few test cases. * If the tests fail, improve the code and repeat the process until the code passes every test.swyx has previously written similar thoughts on types vs tests for putting bounds on program behavior, but AlphaCodium extends this to AI generated tests and code.More recently, Itamar has also shown that AlphaCodium's techniques also extend well to the o1 models:Making Flow Engineering a useful technique to improve code model performance on every model. This is something we see AI Engineers uniquely well positioned to do compared to ML Engineers/Researchers.Full Video PodcastLike and subscribe!Show Notes* Itamar* Qodo* First episode* Eric* Bolt* StackBlitz* Thinkster* AlphaCodium* WebContainersChapters* 00:00:00 Introductions & Updates* 00:06:01 Generic vs. Specific AI Agents* 00:07:40 Maintaining vs Creating with AI* 00:17:46 Human vs Agent Computer Interfaces* 00:20:15 Why Docker doesn't work for Bolt* 00:24:23 Creating Testing and Code Review Loops* 00:28:07 Bolt's Task Breakdown Flow* 00:31:04 AI in Complex Enterprise Environments* 00:41:43 AlphaCodium* 00:44:39 Strategies for Breaking Down Complex Tasks* 00:45:22 Building in Open Source* 00:50:35 Choosing a product as a founder* 00:59:03 Reflections on Bolt Success* 01:06:07 Building a B2C GTM* 01:18:11 AI Capabilities and Pricing Tiers* 01:20:28 What makes Bolt unique* 01:23:07 Future Growth and Product Development* 01:29:06 Competitive Landscape in AI Engineering* 01:30:01 Advice to Founders and Embracing AI* 01:32:20 Having a baby and completing an Iron ManTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.Swyx [00:00:12]: Hey, and today we're still in our sort of makeshift in-between studio, but we're very delighted to have a former returning guest host, Itamar. Welcome back.Itamar [00:00:21]: Great to be here after a year or more. Yeah, a year and a half.Swyx [00:00:24]: You're one of our earliest guests on Agents. Now you're CEO co-founder of Kodo. Right. Which has just been renamed. You also raised a $40 million Series A, and we can get caught up on everything, but we're also delighted to have our new guest, Eric. Welcome.Eric [00:00:42]: Thank you. Excited to be here. Should I say Bolt or StackBlitz?Swyx [00:00:45]: Like, is it like its own company now or?Eric [00:00:47]: Yeah. Bolt's definitely bolt.new. That's the thing that we're probably the most known for, I imagine, at this point.Swyx [00:00:54]: Which is ridiculous to say because you were working at StackBlitz for so long.Eric [00:00:57]: Yeah. I mean, within a week, we were doing like double the amount of traffic. And StackBlitz had been online for seven years, and we were like, what? But anyways, yeah. So we're StackBlitz, the company behind bolt.new. If you've heard of bolt.new, that's our stuff. Yeah.Swyx [00:01:12]: Yeah.Itamar [00:01:13]: Excellent. I see, by the way, that the founder mode, you need to know to capture opportunities. So kudos on doing that, right? You're working on some technology, and then suddenly you can exploit that to a new world. Yeah.Eric [00:01:24]: Totally. And I think, well, not to jump, but 100%, I mean, a couple of months ago, we had the idea for Bolt earlier this year, but we haven't really shared this too much publicly. But we actually had tried to build it with some of those state-of-the-art models back in January, February, you can kind of imagine which, and they just weren't good enough to actually do the code generation where the code was accurate and it was fast and whatever have you without a ton of like rag, but then there was like issues with that. So we put it on the shelf and then we got kind of a sneak peek of some of the new models that have come out in the past couple of months now. And so once we saw that, once we actually saw the code gen from it, we were like, oh my God, like, okay, we can build a product around this. And so that was really the impetus of us building the thing. But with that, it was StackBlitz, the core StackBlitz product the past seven years has been an IDE for developers. So the entire user experience flow we've built up just didn't make sense. And so when we kind of went out to build Bolt, we just thought, you know, if we were inventing our product today, what would the interface look like given what is now possible with the AI code gen? And so there's definitely a lot of conversations we had internally, but you know, just kind of when we logically laid it out, we were like, yeah, I think it makes sense to just greenfield a new thing and let's see what happens. If it works great, then we'll figure it out. If it doesn't work great, then it'll get deleted at some point. So that's kind of how it actually came to be.Swyx [00:02:49]: I'll mention your background a little bit. You were also founder of Thinkster before you started StackBlitz. So both of you are second time founders. Both of you have sort of re-founded your company recently. Yours was more of a rename. I think a slightly different direction as well. And then we can talk about both. Maybe just chronologically, should we get caught up on where Kodo is first and then you know, just like what people should know since the last pod? Sure.Itamar [00:03:12]: The last pod was two months after we launched and we basically had the vision that we talked about. The idea that software development is about specification, test and code, etc. We are more on the testing part as in essence, we think that if you solve testing, you solve software development. The beautiful chart that we'll put up on screen. And testing is a really big field, like there are many dimensions, unit testing, the level of the component, how big it is, how large it is. And then there is like different type of testing, is it regression or smoke or whatever. So back then we only had like one ID extension with unit tests as in focus. One and a half year later, first ID extension supports more type of testing as context aware. We index local, local repos, but also 10,000s of repos for Fortune 500 companies. We have another agent, another tool that is called, the pure agent is the open source and the commercial one is CodoMerge. And then we have another open source called CoverAgent, which is not yet a commercial product coming very soon. It's very impressive. It could be that already people are approving automated pull requests that they don't even aware in really big open sources. So once we have enough of these, we will also launch another agent. So for the first one and a half year, what we did is grew in our offering and mostly on the side of, does this code actually works, testing, code review, et cetera. And we believe that's the critical milestone that needs to be achieved to actually have the AI engineer for enterprise software. And then like for the first year was everything bottom up, getting to 1 million installation. 2024, that was 2023, 2024 was starting to monetize, to feel like how it is to make the first buck. So we did the teams offering, it went well with a thousand of teams, et cetera. And then we started like just a few months ago to do enterprise with everything you need, which is a lot of things that discussed in the last post that was just released by Codelm. So that's how we call it at Codelm. Just opening the brackets, our company name was Codelm AI, and we renamed to Codo and we call our models Codelm. So back to my point, so we started Enterprise Motion and already have multiple Fortune 100 companies. And then with that, we raised a series of $40 million. And what's exciting about it is that enables us to develop more agents. That's our focus. I think it's very different. We're not coming very soon with an ID or something like that.Swyx [00:06:01]: You don't want to fork this code?Itamar [00:06:03]: Maybe we'll fork JetBrains or something just to be different.Swyx [00:06:08]: I noticed that, you know, I think the promise of general purpose agents has kind of died. Like everyone is doing kind of what you're doing. There's Codogen, Codomerge, and then there's a third one. What's the name of it?Itamar [00:06:17]: Yeah. Codocover. Cover. Which is like a commercial version of a cover agent. It's coming soon.Swyx [00:06:23]: Yeah. It's very similar with factory AI, also doing like droids. They all have special purpose doing things, but people don't really want general purpose agents. Right. The last time you were here, we talked about AutoGBT, the biggest thing of 2023. This year, not really relevant anymore. And I think it's mostly just because when you give me a general purpose agent, I don't know what to do with it.Eric [00:06:42]: Yeah.Itamar [00:06:43]: I totally agree with that. We're seeing it for a while and I think it will stay like that despite the computer use, et cetera, that supposedly can just replace us. You can just like prompt it to be, hey, now be a QA or be a QA person or a developer. I still think that there's a few reasons why you see like a dedicated agent. Again, I'm a bit more focused, like my head is more on complex software for big teams and enterprise, et cetera. And even think about permissions and what are the data sources and just the same way you manage permissions for users. Developers, you probably want to have dedicated guardrails and dedicated approvals for agents. I intentionally like touched a point on not many people think about. And of course, then what you can think of, like maybe there's different tools, tool use, et cetera. But just the first point by itself is a good reason why you want to have different agents.Alessio [00:07:40]: Just to compare that with Bot.new, you're almost focused on like the application is very complex and now you need better tools to kind of manage it and build on top of it. On Bot.new, it's almost like I was using it the other day. There's basically like, hey, look, I'm just trying to get started. You know, I'm not very opinionated on like how you're going to implement this. Like this is what I want to do. And you build a beautiful app with it. What people ask as the next step, you know, going back to like the general versus like specific, have you had people say, hey, you know, this is great to start, but then I want a specific Bot.new dot whatever else to do a more vertical integration and kind of like development or what's the, what do people say?Eric [00:08:18]: Yeah. I think, I think you kind of hit the, hit it head on, which is, you know, kind of the way that we've, we've kind of talked about internally is it's like people are using Bolt to go from like 0.0 to 1.0, like that's like kind of the biggest unlock that Bolt has versus most other things out there. I mean, I think that's kind of what's, what's very unique about Bolt. I think the, you know, the working on like existing enterprise applications is, I mean, it's crazy important because, you know, there's a, you look, when you look at the fortune 500, I mean, these code bases, some of these have been around for 20, 30 plus years. And so it's important to be going from, you know, 101.3 to 101.4, et cetera. I think for us, so what's been actually pretty interesting is we see there's kind of two different users for us that are coming in and it's very distinct. It's like people that are developers already. And then there's people that have never really written software and more if they have, it's been very, very minimal. And so in the first camp, what these developers are doing, like to go from zero to one, they're coming to Bolt and then they're ejecting the thing to get up or just downloading it and, you know, opening cursor, like whatever to, to, you know, keep iterating on the thing. And sometimes they'll bring it back to Bolt to like add in a huge piece of functionality or something. Right. But for the people that don't know how to code, they're actually just, they, they live in this thing. And that was one of the weird things when we launched is, you know, within a day of us being online, one of the most popular YouTube videos, and there's been a ton since, which was, you know, there's like, oh, Bolt is the cursor killer. And I originally saw the headlines and I was like, thanks for the views. I mean, I don't know. This doesn't make sense to me. That's not, that's not what we kind of thought.Swyx [00:09:44]: It's how YouTubers talk to each other. Well, everything kills everything else.Eric [00:09:47]: Totally. But what blew my mind was that there was any comparison because it's like cursor is a, is a local IDE product. But when, when we actually kind of dug into it and we, and we have people that are using our product saying this, I'm not using cursor. And I was like, what? And it turns out there are hundreds of thousands of people that we have seen that we're using cursor and we're trying to build apps with that where they're not traditional software does, but we're heavily leaning on the AI. And as you can imagine, it is very complicated, right? To do that with cursor. So when Bolt came out, they're like, wow, this thing's amazing because it kind of inverts the complexity where it's like, you know, it's not an IDE, it's, it's a, it's a chat-based sort of interface that we have. So that's kind of the split, which is rather interesting. We've had like the first startups now launch off of Bolt entirely where this, you know, tomorrow I'm doing a live stream with this guy named Paul, who he's built an entire CRM using this thing and you know, with backend, et cetera. And people have made their first money on the internet period, you know, launching this with Stripe or whatever have you. So that's, that's kind of the two main, the two main categories of folks that we see using Bolt though.Itamar [00:10:51]: I agree that I don't understand the comparison. It doesn't make sense to me. I think like we have like two type of families of tools. One is like we re-imagine the software development. I think Bolt is there and I think like a cursor is more like a evolution of what we already have. It's like taking the IDE and it's, it's amazing and it's okay, let's, let's adapt the IDE to an era where LLMs can do a lot for us. And Bolt is more like, okay, let's rethink everything totally. And I think we see a few tools there, like maybe Vercel, Veo and maybe Repl.it in that area. And then in the area of let's expedite, let's change, let's, let's progress with what we already have. You can see Cursor and Kodo, but we're different between ourselves, Cursor and Kodo, but definitely I think that comparison doesn't make sense.Alessio [00:11:42]: And just to set the context, this is not a Twitter demo. You've made 4 million of revenue in four weeks. So this is, this is actually working, you know, it's not a, what, what do you think that is? Like, there's been so many people demoing coding agents on Twitter and then it doesn't really work. And then you guys were just like, here you go, it's live, go use it, pay us for it. You know, is there anything in the development that was like interesting and maybe how that compares to building your own agents?Eric [00:12:08]: We had no idea, honestly, like we, we, we've been pretty blown away and, and things have just kind of continued to grow faster since then. We're like, oh, today is week six. So I, I kind of came back to the point you just made, right, where it's, you, you kind of outlined, it's like, there's kind of this new market of like kind of rethinking the software development and then there's heavily augmenting existing developers. I think that, you know, both of which are, you know, AI code gen being extremely good, it's allowed existing developers, it's allowing existing developers to camera out software far faster than they could have ever before, right? It's like the ultimate power tool for an existing developer. But this code gen stuff is now so good. And then, and we saw this over the past, you know, from the beginning of the year when we tried to first build, it's actually lowered the barrier to people that, that aren't traditionally software engineers. But the kind of the key thing is if you kind of think about it from, imagine you've never written software before, right? My co-founder and I, he and I grew up down the street from each other in Chicago. We learned how to code when we were 13 together and we've been building stuff ever since. And this is back in like the mid 2000s or whatever, you know, there was nothing for free to learn from online on the internet and how to code. For our 13th birthdays, we asked our parents for, you know, O'Reilly books cause you couldn't get this at the library, right? And so instead of like an Xbox, we got, you know, programming books. But the hardest part for everyone learning to code is getting an environment set up locally, you know? And so when we built StackBlitz, like kind of the key thesis, like seven years ago, the insight we had was that, Hey, it seems like the browser has a lot of new APIs like WebAssembly and service workers, et cetera, where you could actually write an operating system that ran inside the browser that could boot in milliseconds. And you, you know, basically there's this missing capability of the web. Like the web should be able to build apps for the web, right? You should be able to build the web on the web. Every other platform has that, Visual Studio for Windows, Xcode for Mac. The web has no built in primitive for this. And so just like our built in kind of like nerd instinct on this was like, that seems like a huge hole and it's, you know, it will be very valuable or like, you know, very valuable problem to solve. So if you want to set up that environments, you know, this is what we spent the past seven years doing. And the reality is existing developers have running locally. They already know how to set up that environment. So the problem isn't as acute for them. When we put Bolt online, we took that technology called WebContainer and married it with these, you know, state of the art frontier models. And the people that have the most pain with getting stuff set up locally is people that don't code. I think that's been, you know, really the big explosive reason is no one else has been trying to make dev environments work inside of a browser tab, you know, for the past if since ever, other than basically our company, largely because there wasn't an immediate demand or need. So I think we kind of find ourselves at the right place at the right time. And again, for this market of people that don't know how to write software, you would kind of expect that you should be able to do this without downloading something to your computer in the same way that, hey, I don't have to download Photoshop now to make designs because there's Figma. I don't have to download Word because there's, you know, Google Docs. They're kind of looking at this as that sort of thing, right? Which was kind of the, you know, our impetus and kind of vision from the get-go. But you know, the code gen, the AI code gen stuff that's come out has just been, you know, an order of magnitude multiplier on how magic that is, right? So that's kind of my best distillation of like, what is going on here, you know?Alessio [00:15:21]: And you can deploy too, right?Eric [00:15:22]: Yeah.Alessio [00:15:23]: Yeah.Eric [00:15:24]: And so that's, what's really cool is it's, you know, we have deployment built in with Netlify and this is actually, I think, Sean, you actually built this at Netlify when you were there. Yeah. It's one of the most brilliant integrations actually, because, you know, effectively the API that Sean built, maybe you can speak to it, but like as a provider, we can just effectively give files to Netlify without the user even logging in and they have a live website. And if they want to keep, hold onto it, they can click a link and claim it to their Netlify account. But it basically is just this really magic experience because when you come to Bolt, you say, I want a website. Like my mom, 70, 71 years old, made her first website, you know, on the internet two weeks ago, right? It was about her nursing days.Swyx [00:16:03]: Oh, that's fantastic though. It wouldn't have been made.Eric [00:16:06]: A hundred percent. Cause even in, you know, when we've had a lot of people building personal, like deeply personal stuff, like in the first week we launched this, the sales guy from the East Coast, you know, replied to a tweet of mine and he said, thank you so much for building this to your team. His daughter has a medical condition and so for her to travel, she has to like line up donors or something, you know, so ahead of time. And so he actually used Bolt to make a website to do that, to actually go and send it to folks in the region she was going to travel to ahead of time. I was really touched by it, but I also thought like, why, you know, why didn't he use like Wix or Squarespace? Right? I mean, this is, this is a solved problem, quote unquote, right? And then when I thought, I actually use Squarespace for my, for my, uh, the wedding website for my wife and I, like back in 2021, so I'm familiar, you know, it was, it was faster. I know how to code. I was like, this is faster. Right. And I thought back and I was like, there's a whole interface you have to learn how to use. And it's actually not that simple. There's like a million things you can configure in that thing. When you come to Bolt, there's a, there's a text box. You just say, I need a, I need a wedding website. Here's the date. Here's where it is. And here's a photo of me and my wife, put it somewhere relevant. It's actually the simplest way. And that's what my, when my mom came, she said, uh, I'm Pat Simons. I was a nurse in the seventies, you know, and like, here's the things I did and a website came out. So coming back to why is this such a, I think, why are we seeing this sort of growth? It's, this is the simplest interface I think maybe ever created to actually build it, a deploy a website. And then that website, my mom made, she's like, okay, this looks great. And there's, there's one button, you just click it, deploy, and it's live and you can buy a domain name, attach it to it. And you know, it's as simple as it gets, it's getting even simpler with some of the stuff we're working on. But anyways, so that's, it's, it's, uh, it's been really interesting to see some of the usage like that.Swyx [00:17:46]: I can offer my perspective. So I, you know, I probably should have disclosed a little bit that, uh, I'm a, uh, stack list investor.Alessio [00:17:53]: Canceled the episode. I know, I know. Don't play it now. Pause.Eric actually reached out to ShowMeBolt before the launch. And we, you know, we talked a lot about, like, the framing of, of what we're going to talk about how we marketed the thing, but also, like, what we're So that's what Bolt was going to need, like a whole sort of infrastructure.swyx: Netlify, I was a maintainer but I won't take claim for the anonymous upload. That's actually the origin story of Netlify. We can have Matt Billman talk about it, but that was [00:18:00] how Netlify started. You could drag and drop your zip file or folder from your desktop onto a website, it would have a live URL with no sign in.swyx: And so that was the origin story of Netlify. And it just persists to today. And it's just like it's really nice, interesting that both Bolt and CognitionDevIn and a bunch of other sort of agent type startups, they all use Netlify to deploy because of this one feature. They don't really care about the other features.swyx: But, but just because it's easy for computers to use and talk to it, like if you build an interface for computers specifically, that it's easy for them to Navigate, then they will be used in agents. And I think that's a learning that a lot of developer tools companies are having. That's my bolt launch story and now if I say all that stuff.swyx: And I just wanted to come back to, like, the Webcontainers things, right? Like, I think you put a lot of weight on the technical modes. I think you also are just like, very good at product. So you've, you've like, built a better agent than a lot of people, the rest of us, including myself, who have tried to build these things, and we didn't get as far as you did.swyx: Don't shortchange yourself on products. But I think specifically [00:19:00] on, on infra, on like the sandboxing, like this is a thing that people really want. Alessio has Bax E2B, which we'll have on at some point, talking about like the sort of the server full side. But yours is, you know, inside of the browser, serverless.swyx: It doesn't cost you anything to serve one person versus a million people. It doesn't, doesn't cost you anything. I think that's interesting. I think in theory, we should be able to like run tests because you can run the full backend. Like, you can run Git, you can run Node, you can run maybe Python someday.swyx: We talked about this. But ideally, you should be able to have a fully gentic loop, running code, seeing the errors, correcting code, and just kind of self healing, right? Like, I mean, isn't that the dream?Eric: Totally.swyx: Yeah,Eric: totally. At least in bold, we've got, we've got a good amount of that today. I mean, there's a lot more for us to do, but one of the nice things, because like in web container, you know, there's a lot of kind of stuff you go Google like, you know, turn docker container into wasm.Eric: You'll find a lot of stuff out there that will do that. The problem is it's very big, it's slow, and that ruins the experience. And so what we ended up doing is just writing an operating system from [00:20:00] scratch that was just purpose built to, you know, run in a browser tab. And the reason being is, you know, Docker 2 awesome things will give you an image that's like out 60 to 100 megabits, you know, maybe more, you know, and our, our OS, you know, kind of clocks in, I think, I think we're in like a, maybe, maybe a megabyte or less or something like that.Eric: I mean, it's, it's, you know, really, really, you know, stripped down.swyx: This is basically the task involved is I understand that it's. Mapping every single, single Linux call to some kind of web, web assembly implementation,Eric: but more or less, and, and then there's a lot of things actually, like when you're looking at a dev environment, there's a lot of things that you don't need that a traditional OS is gonna have, right?Eric: Like, you know audio drivers or you like, there's just like, there's just tons of things. Oh, yeah. Right. Yeah. That goes . Yeah. You can just kind, you can, you can kind of tos them. Or alternatively, what you can do is you can actually be the nice thing. And this is, this kind of comes back to the origins of browsers, which is, you know, they're, they're at the beginning of the web and, you know, the late nineties, there was two very different kind of visions for the web where Alan Kay vehemently [00:21:00] disagree with the idea that should be document based, which is, you know, Tim Berners Lee, you know, that, and that's kind of what ended up winning, winning was this document based kind of browsing documents on the web thing.Eric: Alan Kay, he's got this like very famous quote where he said, you know, you want web browsers to be mini operating systems. They should download little mini binaries and execute with like a little mini virtualized operating system in there. And what's kind of interesting about the history, not to geek out on this aspect, what's kind of interesting about the history is both of those folks ended up being right.Eric: Documents were actually the pragmatic way that the web worked. Was, you know, became the most ubiquitous platform in the world to the degree now that this is why WebAssembly has been invented is that we're doing, we need to do more low level things in a browser, same thing with WebGPU, et cetera. And so all these APIs, you know, to build an operating system came to the browser.Eric: And that was actually the realization we had in 2017 was, holy heck, like you can actually, you know, service workers, which were designed for allowing your app to work offline. That was the kind of the key one where it was like, wait a second, you can actually now run. Web servers within a [00:22:00] browser, like you can run a server that you open up.Eric: That's wild. Like full Node. js. Full Node. js. Like that capability. Like, I can have a URL that's programmatically controlled. By a web application itself, boom. Like the web can build the web. The primitive is there. Everyone at the time, like we talked to people that like worked on, you know Chrome and V8 and they were like, uhhhh.Eric: You know, like I don't know. But it's one of those things you just kind of have to go do it to find out. So we spent a couple of years, you know, working on it and yeah. And, and, and got to work in back in 2021 is when we kind of put the first like data of web container online. Butswyx: in partnership with Google, right?swyx: Like Google actually had to help you get over the finish line with stuff.Eric: A hundred percent, because well, you know, over the years of when we were doing the R and D on the thing. Kind of the biggest challenge, the two ways that you can kind of test how powerful and capable a platform are, the two types of applications are one, video games, right, because they're just very compute intensive, a lot of calculations that have to happen, right?Eric: The second one are IDEs, because you're talking about actually virtualizing the actual [00:23:00] runtime environment you are in to actually build apps on top of it, which requires sophisticated capabilities, a lot of access to data. You know, a good amount of compute power, right, to effectively, you know, building app in app sort of thing.Eric: So those, those are the stress tests. So if your platform is missing stuff, those are the things where you find out. Those are, those are the people building games and IDEs. They're the ones filing bugs on operating system level stuff. And for us, browser level stuff.Eric [00:23:47]: yeah, what ended up happening is we were just hammering, you know, the Chromium bug tracker, and they're like, who are these guys? Yeah. And, and they were amazing because I mean, just making Chrome DevTools be able to debug, I mean, it's, it's not, it wasn't originally built right for debugging an operating system, right? They've been phenomenal working with us and just kind of really pushing the limits, but that it's a rising tide that's kind of lifted all boats because now there's a lot of different types of applications that you can debug with Chrome Dev Tools that are running a browser that runs more reliably because just the stress testing that, that we and, you know, games that are coming to the web are kind of pushing as well, but.Itamar [00:24:23]: That's awesome. About the testing, I think like most, let's say coding assistant from different kinds will need this loop of testing. And even I would add code review to some, to some extent that you mentioned. How is testing different from code review? Code review could be, for example, PR review, like a code review that is done at the point of when you want to merge branches. But I would say that code review, for example, checks best practices, maintainability, and so on. It's not just like CI, but more than CI. And testing is like a more like checking functionality, et cetera. So it's different. We call, by the way, all of these together code integrity, but that's a different story. Just to go back to the, to the testing and specifically. Yeah. It's, it's, it's since the first slide. Yeah. We're consistent. So if we go back to the testing, I think like, it's not surprising that for us testing is important and for Bolt it's testing important, but I want to shed some light on a different perspective of it. Like let's think about autonomous driving. Those startups that are doing autonomous driving for highway and autonomous driving for the city. And I think like we saw the autonomous of the highway much faster and reaching to a level, I don't know, four or so much faster than those in the city. Now, in both cases, you need testing and quote unquote testing, you know, verifying validation that you're doing the right thing on the road and you're reading and et cetera. But it's probably like so different in the city that it could be like actually different technology. And I claim that we're seeing something similar here. So when you're building the next Wix, and if I was them, I was like looking at you and being a bit scared. That's what you're disrupting, what you just said. Then basically, I would say that, for example, the UX UI is freaking important. And because you're you're more aiming for the end user. In this case, maybe it's an end user that doesn't know how to develop for developers. It's also important. But let alone those that do not know to develop, they need a slick UI UX. And I think like that's one reason, for example, I think Cursor have like really good technology. I don't know the underlying what's under the hood, but at least what they're saying. But I think also their UX UI is great. It's a lot because they did their own ID. While if you're aiming for the city AI, suddenly like there's a lot of testing and code review technology that it's not necessarily like that important. For example, let's talk about integration tests. Probably like a lot of what you're building involved at the moment is isolated applications. Maybe the vision or the end game is maybe like having one solution for everything. It could be that eventually the highway companies will go into the city and the other way around. But at the beginning, there is a difference. And integration tests are a good example. I guess they're a bit less important. And when you think about enterprise software, they're really important. So to recap, like I think like the idea of looping and verifying your test and verifying your code in different ways, testing or code review, et cetera, seems to be important in the highway AI and the city AI, but in different ways and different like critical for the city, even more and more variety. Actually, I was looking to ask you like what kind of loops you guys are doing. For example, when I'm using Bolt and I'm enjoying it a lot, then I do see like sometimes you're trying to catch the errors and fix them. And also, I noticed that you're breaking down tasks into smaller ones and then et cetera, which is already a common notion for a year ago. But it seems like you're doing it really well. So if you're willing to share anything about it.Eric [00:28:07]: Yeah, yeah. I realized I never actually hit the punchline of what I was saying before. I mentioned the point about us kind of writing an operating system from scratch because what ended up being important about that is that to your point, it's actually a very, like compared to like a, you know, if you're like running cursor on anyone's machine, you kind of don't know what you're dealing with, with the OS you're running on. There could be an error happens. It could be like a million different things, right? There could be some config. There could be, it could be God knows what, right? The thing with WebConnect is because we wrote the entire thing from scratch. It's actually a unified image basically. And we can instrument it at any level that we think is going to be useful, which is exactly what we did when we started building Bolt is we instrumented stuff at like the process level, at the runtime level, you know, et cetera, et cetera, et cetera. Stuff that would just be not impossible to do on local, but to do that in a way that works across any operating system, whatever is, I mean, would just be insanely, you know, insanely difficult to do right and reliably. And that's what you saw when you've used Bolt is that when an error actually will occur, whether it's in the build process or the actual web application itself is failing or anything kind of in between, you can actually capture those errors. And today it's a very primitive way of how we've implemented it largely because the product just didn't exist 90 days ago. So we're like, we got some work ahead of us and we got to hire some more a little bit, but basically we present and we say, Hey, this is, here's kind of the things that went wrong. There's a fix it button and then a ignore button, and then you can just hit fix it. And then we take all that telemetry through our agent, you run it through our agent and say, kind of, here's the state of the application. Here's kind of the errors that we got from Node.js or the browser or whatever, and like dah, dah, dah, dah. And it can take a crack at actually solving it. And it's actually pretty darn good at being able to do that. That's kind of been a, you know, closing the loop and having it be a reliable kind of base has seemed to be a pretty big upgrade over doing stuff locally, just because I think that's a pretty key ingredient of it. And yeah, I think breaking things down into smaller tasks, like that's, that's kind of a key part of our agent. I think like Claude did a really good job with artifacts. I think, you know, us and kind of everyone else has, has kind of taken their approach of like actually breaking out certain tasks in a certain order into, you know, kind of a concrete way. And, and so actually the core of Bolt, I know we actually made open source. So you can actually go and check out like the system prompts and et cetera, and you can run it locally and whatever have you. So anyone that's interested in this stuff, I'd highly recommend taking a look at. There's not a lot of like stuff that's like open source in this realm. It's, that was one of the fun things that we've we thought would be cool to do. And people, people seem to like it. I mean, there's a lot of forks and people adding different models and stuff. So it's been cool to see.Swyx [00:30:41]: Yeah. I'm happy to add, I added real-time voice for my opening day demo and it was really fun to hack with. So thank you for doing that. Yeah. Thank you. I'm going to steal your code.Eric [00:30:52]: Because I want that.Swyx [00:30:52]: It's funny because I built on top of the fork of Bolt.new that already has the multi LLM thing. And so you just told me you're going to merge that in. So then you're going to merge two layers of forks down into this thing. So it'll be fun.Eric [00:31:03]: Heck yeah.Alessio [00:31:04]: Just to touch on like the environment, Itamar, you maybe go into the most complicated environments that even the people that work there don't know how to run. How much of an impact does that have on your performance? Like, you know, it's most of the work you're doing actually figuring out environment and like the libraries, because I'm sure they're using outdated version of languages, they're using outdated libraries, they're using forks that have not been on the public internet before. How much of the work that you're doing is like there versus like at the LLM level?Itamar [00:31:32]: One of the reasons I was asking about, you know, what are the steps to break things down, because it really matters. Like, what's the tech stack? How complicated the software is? It's hard to figure it out when you're dealing with the real world, any environment of enterprise as a city, when I'm like, while maybe sometimes like, I think you do enable like in Bolt, like to install stuff, but it's quite a like controlled environment. And that's a good thing to do, because then you narrow down and it's easier to make things work. So definitely, there are two dimensions, I think, actually spaces. One is the fact just like installing our software without yet like doing anything, making it work, just installing it because we work with enterprise and Fortune 500, etc. Many of them want on prem solution.Swyx [00:32:22]: So you have how many deployment options?Itamar [00:32:24]: Basically, we had, we did a metric metrics, say 96 options, because, you know, they're different dimensions. Like, for example, one dimension, we connect to your code management system to your Git. So are you having like GitHub, GitLab? Subversion? Is it like on cloud or deployed on prem? Just an example. Which model agree to use its APIs or ours? Like we have our Is it TestGPT? Yeah, when we started with TestGPT, it was a huge mistake name. It was cool back then, but I don't think it's a good idea to name a model after someone else's model. Anyway, that's my opinion. So we gotSwyx [00:33:02]: I'm interested in these learnings, like things that you change your mind on.Itamar [00:33:06]: Eventually, when you're building a company, you're building a brand and you want to create your own brand. By the way, when I thought about Bolt.new, I also thought about if it's not a problem, because when I think about Bolt, I do think about like a couple of companies that are already called this way.Swyx [00:33:19]: Curse companies. You could call it Codium just to...Itamar [00:33:24]: Okay, thank you. Touche. Touche.Eric [00:33:27]: Yeah, you got to imagine the board meeting before we launched Bolt, one of our investors, you can imagine they're like, are you sure? Because from the investment side, it's kind of a famous, very notorious Bolt. And they're like, are you sure you want to go with that name? Oh, yeah. Yeah, absolutely.Itamar [00:33:43]: At this point, we have actually four models. There is a model for autocomplete. There's a model for the chat. There is a model dedicated for more for code review. And there is a model that is for code embedding. Actually, you might notice that there isn't a good code embedding model out there. Can you name one? Like dedicated for code?Swyx [00:34:04]: There's code indexing, and then you can do sort of like the hide for code. And then you can embed the descriptions of the code.Itamar [00:34:12]: Yeah, but you do see a lot of type of models that are dedicated for embedding and for different spaces, different fields, etc. And I'm not aware. And I know that if you go to the bedrock, try to find like there's a few code embedding models, but none of them are specialized for code.Swyx [00:34:31]: Is there a benchmark that you would tell us to pay attention to?Itamar [00:34:34]: Yeah, so it's coming. Wait for that. Anyway, we have our models. And just to go back to the 96 option of deployment. So I'm closing the brackets for us. So one is like dimensional, like what Git deployment you have, like what models do you agree to use? Dotter could be like if it's air-gapped completely, or you want VPC, and then you have Azure, GCP, and AWS, which is different. Do you use Kubernetes or do not? Because we want to exploit that. There are companies that do not do that, etc. I guess you know what I mean. So that's one thing. And considering that we are dealing with one of all four enterprises, we needed to deal with that. So you asked me about how complicated it is to solve that complex code. I said, it's just a deployment part. And then now to the software, we see a lot of different challenges. For example, some companies, they did actually a good job to build a lot of microservices. Let's not get to if it's good or not, but let's first assume that it is a good thing. A lot of microservices, each one of them has their own repo. And now you have tens of thousands of repos. And you as a developer want to develop something. And I remember me coming to a corporate for the first time. I don't know where to look at, like where to find things. So just doing a good indexing for that is like a challenge. And moreover, the regular indexing, the one that you can find, we wrote a few blogs on that. By the way, we also have some open source, different than yours, but actually three and growing. Then it doesn't work. You need to let the tech leads and the companies influence your indexing. For example, Mark with different repos with different colors. This is a high quality repo. This is a lower quality repo. This is a repo that we want to deprecate. This is a repo we want to grow, etc. And let that be part of your indexing. And only then things actually work for enterprise and they don't get to a fatigue of, oh, this is awesome. Oh, but I'm starting, it's annoying me. I think Copilot is an amazing tool, but I'm quoting others, meaning GitHub Copilot, that they see not so good retention of GitHub Copilot and enterprise. Ooh, spicy. Yeah. I saw snapshots of people and we have customers that are Copilot users as well. And also I saw research, some of them is public by the way, between 38 to 50% retention for users using Copilot and enterprise. So it's not so good. By the way, I don't think it's that bad, but it's not so good. So I think that's a reason because, yeah, it helps you auto-complete, but then, and especially if you're working on your repo alone, but if it's need that context of remote repos that you're code-based, that's hard. So to make things work, there's a lot of work on that, like giving the controllability for the tech leads, for the developer platform or developer experience department in the organization to influence how things are working. A short example, because if you have like really old legacy code, probably some of it is not so good anymore. If you just fine tune on these code base, then there is a bias to repeat those mistakes or old practices, etc. So you need, for example, as I mentioned, to influence that. For example, in Coda, you can have a markdown of best practices by the tech leads and Coda will include that and relate to that and will not offer suggestions that are not according to the best practices, just as an example. So that's just a short list of things that you need to do in order to deal with, like you mentioned, the 100.1 to 100.2 version of software. I just want to say what you're doing is extremelyEric [00:38:32]: impressive because it's very difficult. I mean, the business of Stackplus, kind of before bulk came online, we sold a version of our IDE that went on-prem. So I understand what you're saying about the difficulty of getting stuff just working on-prem. Holy heck. I mean, that is extremely hard. I guess the question I have for you is, I mean, we were just doing that with kind of Kubernetes-based stuff, but the spread of Fortune 500 companies that you're working with, how are they doing the inference for this? Are you kind of plugging into Azure's OpenAI stuff and AWS's Bedrock, you know, Cloud stuff? Or are they just like running stuff on GPUs? Like, what is that? How are these folks approaching that? Because, man, what we saw on the enterprise side, I mean, I got to imagine that that's a huge challenge. Everything you said and more, like,Itamar [00:39:15]: for example, like someone could be, and I don't think any of these is bad. Like, they made their decision. Like, for example, some people, they're, I want only AWS and VPC on AWS, no matter what. And then they, some of them, like there is a subset, I will say, I'm willing to take models only for from Bedrock and not ours. And we have a problem because there is no good code embedding model on Bedrock. And that's part of what we're doing now with AWS to solve that. We solve it in a different way. But if you are willing to run on AWS VPC, but run your run models on GPUs or inferentia, like the new version of the more coming out, then our models can run on that. But everything you said is right. Like, we see like on-prem deployment where they have their own GPUs. We see Azure where you're using OpenAI Azure. We see cases where you're running on GCP and they want OpenAI. Like this cross, like a case, although there is Gemini or even Sonnet, I think is available on GCP, just an example. So all the options, that's part of the challenge. I admit that we thought about it, but it was even more complicated. And it took us a few months to actually, that metrics that I mentioned, to start clicking each one of the blocks there. A few months is impressive. I mean,Eric [00:40:35]: honestly, just that's okay. Every one of these enterprises is, their networking is different. Just everything's different. Every single one is different. I see you understand. Yeah. So that just cannot be understated. That it is, that's extremely impressive. Hats off.Itamar [00:40:50]: It could be, by the way, like, for example, oh, we're only AWS, but our GitHub enterprise is on-prem. Oh, we forgot. So we need like a private link or whatever, like every time like that. It's not, and you do need to think about it if you want to work with an enterprise. And it's important. Like I understand like their, I respect their point of view.Swyx [00:41:10]: And this primarily impacts your architecture, your tech choices. Like you have to, you can't choose some vendors because...Itamar [00:41:15]: Yeah, definitely. To be frank, it makes us hard for a startup because it means that we want, we want everyone to enjoy all the variety of models. By the way, it was hard for us with our technology. I want to open a bracket, like a window. I guess you're familiar with our Alpha Codium, which is an open source.Eric [00:41:33]: We got to go over that. Yeah. So I'll do that quickly.Itamar [00:41:36]: Yeah. A pin in that. Yeah. Actually, we didn't have it in the last episode. So, so, okay.Swyx [00:41:41]: Okay. We'll come back to that later, but let's talk about...Itamar [00:41:43]: Yeah. So, so just like shortly, and then we can double click on Alpha Codium. But Alpha Codium is a open source tool. You can go and try it and lets you compete on CodeForce. This is a website and a competition and actually reach a master level level, like 95% with a click of a button. You don't need to do anything. And part of what we did there is taking a problem and breaking it to different, like smaller blocks. And then the models are doing a much better job. Like we all know it by now that taking small tasks and solving them, by the way, even O1, which is supposed to be able to do system two thinking like Greg from OpenAI like hinted, is doing better on these kinds of problems. But still, it's very useful to break it down for O1, despite O1 being able to think by itself. And that's what we presented like just a month ago, OpenAI released that now they are doing 93 percentile with O1 IOI left and International Olympiad of Formation. Sorry, I forgot. Exactly. I told you I forgot. And we took their O1 preview with Alpha Codium and did better. Like it just shows like, and there is a big difference between the preview and the IOI. It shows like that these models are not still system two thinkers, and there is a big difference. So maybe they're not complete system two. Yeah, they need some guidance. I call them system 1.5. We can, we can have it. I thought about it. Like, you know, I care about this philosophy stuff. And I think like we didn't see it even close to a system two thinking. I can elaborate later. But closing the brackets, like we take Alpha Codium and as our principle of thinking, we take tasks and break them down to smaller tasks. And then we want to exploit the best model to solve them. So I want to enable anyone to enjoy O1 and SONET and Gemini 1.5, etc. But at the same time, I need to develop my own models as well, because some of the Fortune 500 want to have all air gapped or whatever. So that's a challenge. Now you need to support so many models. And to some extent, I would say that the flow engineering, the breaking down to two different blocks is a necessity for us. Why? Because when you take a big block, a big problem, you need a very different prompt for each one of the models to actually work. But when you take a big problem and break it into small tasks, we can talk how we do that, then the prompt matters less. What I want to say, like all this, like as a startup trying to do different deployment, getting all the juice that you can get from models, etc. is a big problem. And one need to think about it. And one of our mitigation is that process of taking tasks and breaking them down. That's why I'm really interested to know how you guys are doing it. And part of what we do is also open source. So you can see.Swyx [00:44:39]: There's a lot in there. But yeah, flow over prompt. I do believe that that does make sense. I feel like there's a lot that both of you can sort of exchange notes on breaking down problems. And I just want you guys to just go for it. This is fun to watch.Eric [00:44:55]: Yeah. I mean, what's super interesting is the context you're working in is, because for us too with Bolt, we've started thinking because our kind of existing business line was going behind the firewall, right? We were like, how do we do this? Adding the inference aspect on, we're like, okay, how does... Because I mean, there's not a lot of prior art, right? I mean, this is all new. This is all new. So I definitely am going to have a lot of questions for you.Itamar [00:45:17]: I'm here. We're very open, by the way. We have a paper on a blog or like whatever.Swyx [00:45:22]: The Alphacodeum, GitHub, and we'll put all this in the show notes.Itamar [00:45:25]: Yeah. And even the new results of O1, we published it.Eric [00:45:29]: I love that. And I also just, I think spiritually, I like your approach of being transparent. Because I think there's a lot of hype-ium around AI stuff. And a lot of it is, it's just like, you have these companies that are just kind of keep their stuff closed source and then just max hype it, but then it's kind of nothing. And I think it kind of gives a bad rep to the incredible stuff that's actually happening here. And so I think it's stuff like what you're doing where, I mean, true merit and you're cracking open actual code for others to learn from and use. That strikes me as the right approach. And it's great to hear that you're making such incredible progress.Itamar [00:46:02]: I have something to share about the open source. Most of our tools are, we have an open source version and then a premium pro version. But it's not an easy decision to do that. I actually wanted to ask you about your strategy, but I think in your case, there is, in my opinion, relatively a good strategy where a lot of parts of open source, but then you have the deployment and the environment, which is not right if I get it correctly. And then there's a clear, almost hugging face model. Yeah, you can do that, but why should you try to deploy it yourself, deploy it with us? But in our case, and I'm not sure you're not going to hit also some competitors, and I guess you are. I wanted to ask you, for example, on some of them. In our case, one day we looked on one of our competitors that is doing code review. We're a platform. We have the code review, the testing, et cetera, spread over the ID to get. And in each agent, we have a few startups or a big incumbents that are doing only that. So we noticed one of our competitors having not only a very similar UI of our open source, but actually even our typo. And you sit there and you're kind of like, yeah, we're not that good. We don't use enough Grammarly or whatever. And we had a couple of these and we saw it there. And then it's a challenge. And I want to ask you, Bald is doing so well, and then you open source it. So I think I know what my answer was. I gave it before, but still interestingEric [00:47:29]: to hear what you think. GeoHot said back, I don't know who he was up to at this exact moment, but I think on comma AI, all that stuff's open source. And someone had asked him, why is this open source? And he's like, if you're not actually confident that you can go and crush it and build the best thing, then yeah, you should probably keep your stuff closed source. He said something akin to that. I'm probably kind of butchering it, but I thought it was kind of a really good point. And that's not to say that you should just open source everything, because for obvious reasons, there's kind of strategic things you have to kind of take in mind. But I actually think a pretty liberal approach, as liberal as you kind of can be, it can really make a lot of sense. Because that is so validating that one of your competitors is taking your stuff and they're like, yeah, let's just kind of tweak the styles. I mean, clearly, right? I think it's kind of healthy because it keeps, I'm sure back at HQ that day when you saw that, you're like, oh, all right, well, we have to grind even harder to make sure we stay ahead. And so I think it's actually a very useful, motivating thing for the teams. Because you might feel this period of comfort. I think a lot of companies will have this period of comfort where they're not feeling the competition and one day they get disrupted. So kind of putting stuff out there and letting people push it forces you to face reality soon, right? And actually feel that incrementally so you can kind of adjust course. And that's for us, the open source version of Bolt has had a lot of features people have been begging us for, like persisting chat messages and checkpoints and stuff. Within the first week, that stuff was landed in the open source versions. And they're like, why can't you ship this? It's in the open, so people have forked it. And we're like, we're trying to keep our servers and GPUs online. But it's been great because the folks in the community did a great job, kept us on our toes. And we've got to know most of these folks too at this point that have been building these things. And so it actually was very instructive. Like, okay, well, if we're going to go kind of land this, there's some UX patterns we can kind of look at and the code is open source to this stuff. What's great about these, what's not. So anyways, NetNet, I think it's awesome. I think from a competitive point of view for us, I think in particular, what's interesting is the core technology of WebContainer going. And I think that right now, there's really nothing that's kind of on par with that. And we also, we have a business of, because WebContainer runs in your browser, but to make it work, you have to install stuff from NPM. You have to make cores bypass requests, like connected databases, which all require server-side proxying or acceleration. And so we actually sell WebContainer as a service. One of the core reasons we open-sourced kind of the core components of Bolt when we launched was that we think that there's going to be a lot more of these AI, in-your-browser AI co-gen experiences, kind of like what Anthropic did with Artifacts and Clod. By the way, Artifacts uses WebContainers. Not yet. No, yeah. Should I strike that? I think that they've got their own thing at the moment, but there's been a lot of interest in WebContainers from folks doing things in that sort of realm and in the AI labs and startups and everything in between. So I think there'll be, I imagine, over the coming months, there'll be lots of things being announced to folks kind of adopting it. But yeah, I think effectively...Swyx [00:50:35]: Okay, I'll say this. If you're a large model lab and you want to build sandbox environments inside of your chat app, you should call Eric.Itamar [00:50:43]: But wait, wait, wait, wait, wait, wait. I have a question about that. I think OpenAI, they felt that people are not using their model as they would want to. So they built ChatGPT. But I would say that ChatGPT now defines OpenAI. I know they're doing a lot of business from their APIs, but still, is this how you think? Isn't Bolt.new your business now? Why don't you focus on that instead of the...Swyx [00:51:16]: What's your advice as a founder?Eric [00:51:18]: You're right. And so going into it, we, candidly, we were like, Bolt.new, this thing is super cool. We think people are stoked. We think people will be stoked. But we were like, maybe that's allowed. Best case scenario, after month one, we'd be mind blown if we added a couple hundred K of error or something. And we were like, but we think there's probably going to be an immediate huge business. Because there was some early poll on folks wanting to put WebContainer into their product offerings, kind of similar to what Bolt is doing or whatever. We were actually prepared for the inverse outcome here. But I mean, well, I guess we've seen poll on both. But I mean, what's happened with Bolt, and you're right, it's actually the same strategy as like OpenAI or Anthropic, where we have our ChatGPT to OpenAI's APIs is Bolt to WebContainer. And so we've kind of taken that same approach. And we're seeing, I guess, some of the similar results, except right now, the revenue side is extremely lopsided to Bolt.Itamar [00:52:16]: I think if you ask me what's my advice, I think you have three options. One is to focus on Bolt. The other is to focus on the WebContainer. The third is to raise one billion dollars and do them both. I'm serious. I think otherwise, you need to choose. And if you raise enough money, and I think it's big bucks, because you're going to be chased by competitors. And I think it will be challenging to do both. And maybe you can. I don't know. We do see these numbers right now, raising above $100 million, even without havingEric [00:52:49]: a product. You can see these. It's excellent advice. And I think what's been amazing, but also kind of challenging is we're trying to forecast, okay, well, where are these things going? I mean, in the initial weeks, I think us and all the investors in the company that we're sharing this with, it was like, this is cool. Okay, we added 500k. Wow, that's crazy. Wow, we're at a million now. Most things, you have this kind of the tech crunch launch of initiation and then the thing of sorrow. And if there's going to be a downtrend, it's just not coming yet. Now that we're kind of looking ahead, we're six weeks in. So now we're getting enough confidence in our convictions to go, okay, this se

Episode 32: How can you build an app with a backend using AI in just 20 minutes? Matt Wolfe (https://x.com/mreflow) and Nathan Lands (https://x.com/NathanLands) sit down with AI enthusiast Riley Brown (https://x.com/rileybrown_ai) to explore this exciting and challenging process. In this episode, Riley brings his unique perspective and experience, from a non-coder to a developer leveraging AI tools. The discussion covers Riley's journey, the tools he recommends for beginners, like Cursor and Replit, and the integration with Firebase for seamless app development. They venture into creating a simple web app, discuss the evolution of app capabilities, and contemplate innovative features and platforms driven by AI. Whether you're a novice or an experienced developer, this episode offers a wealth of insights and practical advice. Check out The Next Wave YouTube Channel if you want to see Matt and Nathan on screen: https://lnk.to/thenextwavepd — Show Notes: (00:00) Riley Brown shares app-building methods, templates. (04:15) Using Claude artifacts for code generation amazed me. (08:35) Start with Cursor, avoid multiple tool distractions. (09:34) Codebase setup using SSH for syncing changes. (12:55) AI integrates and updates code in steps. (17:49) App to log and track AI skill development. (20:04) Tools: Cursor, Firebase, Replit for project management. (25:12) Discusses free use of Replit, Firebase, Cursor. (27:32) App for threading voice notes and AI formatting. (30:58) Appreciating design effort; seeking AI improvement. (33:31) Building community to create apps efficiently. (35:20) Follow Riley Brown on X, subscribe YouTube. — Mentions: Riley Brown: https://community.softwarecomposer.com/c/templates/replit-templates https://replit.com/@an732001/Riley-and-Ansh-Full-Stack-Nextjs-Template-version-1?v=1#README.md Software Composer: https://www.softwarecomposer.com/ Cursor: https://www.cursor.so/ Replit: https://replit.com/ Firebase: https://firebase.google.com/ Midjourney: https://www.midjourney.com/ Claude: https://www.anthropic.com/index/claude Wispr Flow: https://www.flowvoice.ai/ — Check Out Matt's Stuff: • Future Tools - https://futuretools.beehiiv.com/ • Blog - https://www.mattwolfe.com/ • YouTube- https://www.youtube.com/@mreflow — Check Out Nathan's Stuff: Newsletter: https://news.lore.com/ Blog - https://lore.com/ The Next Wave is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Darren Clarke // Editing by Ezra Bakker Trupiano

Episode 29: Are you ready to dive into the world of AI-driven newsletter creation and content strategy? Matt Wolfe (https://x.com/mreflow) and Nathan Lands (https://x.com/NathanLands) discuss the tools, techniques, and insider secrets to building a successful AI-powered business from scratch. In this episode, they explore AI's role in streamlining newsletter creation, bundling media properties for better monetization, and maintaining the crucial human touch for quality and engagement. Plus, they share their personal experiences and strategies for leveraging AI tools like Perplexity, Claude, Mixo, and many more to validate business ideas and enhance content production. Check out The Next Wave YouTube Channel if you want to see Matt and Nathan on screen: https://lnk.to/thenextwavepd — Show Notes: (00:00) Leverage AI tools, don't start AI businesses. (03:43) Mixo creates instant landing pages using prompts. (09:23) Firebase, Replit, and AI simplify business startup. (12:54) Amazon sellers use sentiment analysis to improve products. (14:14) Focus on human-centric content creation amid AI. (18:17) Mix of memorization and automated video editing. (22:12) AI-generated avatars as news anchors increasing. (24:48) AI simplifies newsletter creation with writing, editing. (28:50) Newsletters need unique voices for long-term success. (32:01) Timing was crucial for YouTube success. (34:49) Automated tool summary solution using Perplexity. (38:56) Covered strategies, tools, and ideas for businesses. — Mentions: Mixo: https://www.mixo.io/ Perplexity: https://www.perplexity.ai/ Claude: https://claude.ai/ Futureloop: https://www.diamandis.com/futureloop Timebolt: https://www.timebolt.io/ Firebase: https://firebase.google.com/ Cursor: https://www.cursor.com/ Webflow: https://webflow.com/ — Check Out Matt's Stuff: • Future Tools - https://futuretools.beehiiv.com/ • Blog - https://www.mattwolfe.com/ • YouTube- https://www.youtube.com/@mreflow — Check Out Nathan's Stuff: Newsletter: https://news.lore.com/ Blog - https://lore.com/ The Next Wave is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Darren Clarke // Editing by Ezra Bakker Trupiano

The grand finale of BTSeptember finds Brian & Cargill on a tour of duty through hell as they try and survive The Siege of Firebase Gloria.Support us on Patreon! 

Scott and CJ chat with Paul Copplestone, CEO and co-founder of Supabase, about the journey of building an open source alternative to Firebase. Learn about the tech stack, the story behind their excellent documentation, and how Supabase balances business goals with open-source values. Show Notes 00:00 Welcome to Syntax! 00:30 Who is Paul Copplestone? 01:17 Why ‘Supa' and not ‘Super'? 02:26 How did Supabase start? 04:29 How long from inception to joining Y Combinator? 05:10 Was it always intended to be open source? Why Open Source. 07:22 How many users chose to self-host? 07:49 Open source mindset. 08:42 Simplicity in design. 10:32 How do you take Supabase one step beyond the competition? 12:35 How do you decide which libraries are officially supported vs community maintained? 15:17 You don't need a client library! 16:48 Edge functions for server-side functionality. 18:51 The genesis of pgvector. 20:59 The product strategy. 22:25 What's the story behind Supabase's awesome docs? 25:26 The tech behind Supabase. 25:39 What is the UI built on? 27:33 Consolidation follows kaizen. 28:54 What else is involved in the stack? 31:47 Authentication. 32:35 Storage engine. 33:13 For self-hosting. 35:46 How do you balance business goals with open source? 42:01 What's next for Supabase? 44:15 Supabase's GA + new features. Top 10 LAunches from Supabase GA Week. 48:24 Who runs the X account? 50:39 Sick Picks + Shameless Plugs. Sick Picks Paul: Apple Vision Pro. Shameless Plugs Paul: PostgreSQL. Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads CJ: X Instagram YouTube TwitchTV Randy: X Instagram YouTube Threads

In this episode, we delve into the intricacies of Google UAC strategies on iOS post Apple's introduction of App Tracking Transparency (ATT). We love this episode as this is from an ex Googler who gives us the inside track into the dynamics at play within Google that shaped a lot of the changes that happened post ATT. Ashley Black describes details about Google's initial response to ATT, and talks about modeled conversions. We also uncover the challenges and limitations Google faces with iOS ad inventory, particularly the inability to prompt for ATT in browsers and the significant enhancements brought about by SKAN4, including web-to-app tracking. Tune in to gain a deeper understanding of how Google maneuvers through changes like those that happened with ATT on iOS - and, more importantly, how you can adapt to a post-ATT world on iOS with UAC.KEY HIGHLIGHTS

Summary Generative AI has rapidly transformed everything in the technology sector. When Andrew Lee started work on Shortwave he was focused on making email more productive. When AI started gaining adoption he realized that he had even more potential for a transformative experience. In this episode he shares the technical challenges that he and his team have overcome in integrating AI into their product, as well as the benefits and features that it provides to their customers. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Andrew Lee about his work on Shortwave, an AI powered email client Interview Introduction How did you get involved in the area of data management? Can you describe what Shortwave is and the story behind it? What is the core problem that you are addressing with Shortwave? Email has been a central part of communication and business productivity for decades now. What are the overall themes that continue to be problematic? What are the strengths that email maintains as a protocol and ecosystem? From a product perspective, what are the data challenges that are posed by email? Can you describe how you have architected the Shortwave platform? How have the design and goals of the product changed since you started it? What are the ways that the advent and evolution of language models have influenced your product roadmap? How do you manage the personalization of the AI functionality in your system for each user/team? For users and teams who are using Shortwave, how does it change their workflow and communication patterns? Can you describe how I would use Shortwave for managing the workflow of evaluating, planning, and promoting my podcast episodes? What are the most interesting, innovative, or unexpected ways that you have seen Shortwave used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Shortwave? When is Shortwave the wrong choice? What do you have planned for the future of Shortwave? Contact Info LinkedIn (https://www.linkedin.com/in/startupandrew/) Blog (https://startupandrew.com/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links Shortwave (https://www.shortwave.com/) Firebase (https://firebase.google.com/) Google Inbox (https://en.wikipedia.org/wiki/Inbox_by_Gmail) Hey (https://www.hey.com/) Ezra Klein Hey Article (https://www.nytimes.com/2024/04/07/opinion/gmail-email-digital-shame.html) Superhuman (https://superhuman.com/) Pinecone (https://www.pinecone.io/) Podcast Episode (https://www.dataengineeringpodcast.com/pinecone-vector-database-similarity-search-episode-189/) Elastic (https://www.elastic.co/) Hybrid Search (https://weaviate.io/blog/hybrid-search-explained) Semantic Search (https://en.wikipedia.org/wiki/Semantic_search) Mistral (https://mistral.ai/) GPT 3.5 (https://platform.openai.com/docs/models/gpt-3-5-turbo) IMAP (https://en.wikipedia.org/wiki/Internet_Message_Access_Protocol) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

Dive into the intricacies of campaign optimization as we explore strategies to attract the most valuable users. Discover how prioritizing high-value users impacts your cost per install (CPI) and unlocks the potential for top-tier paying users. The Setup Phase: Mobilizing Machine Learning with Mobile App Campaigns Learn the cost-effective approach of kickstarting machine learning with mobile app campaigns. Understand the significance of importing events from your mobile measurement partner (MMP) or Firebase into platforms like Google Ads to pinpoint user-triggered events and strategically detect drop-offs. Evolution to App Event Optimization Campaigns Explore the transition to advanced app event optimization campaigns, focusing on specific high-engagement events. Delve into the analysis of campaign performance against profitability goals and the importance of streamlining event selection for clarity and effectiveness. Simplicity is Key: Streamlining Event Selection Uncover the power of simplicity in event selection, with a spotlight on essential events like purchases. Avoid the pitfalls of unnecessary complexity to maintain campaign clarity and enhance profitability. Instagram Ads Effectiveness: Target Audience Insights Get insights into the effectiveness of Instagram ads by considering the age demographics of your target audience. Understand why Facebook might outperform Instagram for an older audience and how static banners influence algorithmic targeting. In Conclusion: Navigating Complexity for Optimal Performance Wrap up the discussion with a holistic view of navigating campaign optimization. From mobile app campaigns to sophisticated event-based strategies, learn how to refine your campaigns and achieve optimal performance. You can also watch the video: https://youtu.be/9G_lK37G6Ys Work with us to grow your apps faster & cheaper: ⁠http://www.appmasters.com/⁠ SPONSORS Tired of overpaying for App Store Optimization? Get unlimited ASO and app marketing support to increase your keyword rankings, downloads, and revenue. Learn more at ⁠ASO Masters⁠. *************** Follow us: YouTube: ⁠AppMasters.com/YouTube⁠ Instagram: ⁠@stevepyoung⁠ Twitter: ⁠@stevepyoung⁠ TikTok: ⁠@stevepyoung⁠ Facebook: ⁠App Masters⁠ *************** --- Send in a voice message: https://podcasters.spotify.com/pod/show/app-marketing-podcast/message

The House Unanimously Passes a Bill to Halt Sale of American Data to Foreign Foes. The U.S. Sanctions Russian Individuals and Entities for a Global Disinformation Campaign. China warns of cyber threats from foreign hacking groups. A logistics firm isolates its Canadian division after a cyber attack. Ivanti warns of another critical vulnerability. Researchers find hundreds of vulnerable Firebase instances. Microsoft phases out weaker encryption. Formula One fans fight phishing in the fast lane. Glassdoor is accused of adding real names to profiles without user consent. Our guest is Adam Meyers, SVP of Counter Adversary Operations at CrowdStrike, discussing how adversaries are attacking cloud environments and why it's an increasingly popular attack surface. And Pwn2Own winners take home their second Tesla.  Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Guest Adam Meyers, SVP of Counter Adversary Operations at CrowdStrike, discussing how adversaries are attacking cloud environments and why it's an increasingly popular attack surface – especially as more companies implement AI. For more information, check out CrowdStrike's 2024 Global Threat Report.  Selected Reading House unanimously passes bill to block data brokers from selling Americans' info to foreign adversaries (The Record) Treasury Sanctions Actors Supporting Kremlin-Directed Malign Influence Efforts (US Treasury Department) China warns foreign hackers are infiltrating ‘hundreds' of business and government networks (SCMP) International freight tech firm isolates Canada operations after cyberattack (The Record) Ivanti urges customers to fix critical RCE flaw in Standalone Sentry solution (Security Affairs) 19 million plaintext passwords exposed by incorrectly configured Firebase instances (Malwarebytes) Microsoft deprecates 1024-bit Windows RSA keys — now would be a good time to get machine identity management in order (ITPro) Users ditch Glassdoor, stunned by site adding real names without consent (Ars Technica) Famous Spa GP F1 race comms hijacked by phishing scammers (Cyber Daily) Security Researchers Win Second Tesla At Pwn2Own (Infosecurity Magazine) Share your feedback. We want to ensure that you are getting the most out of the podcast. Please take a few minutes to share your thoughts with us by completing our brief listener survey as we continually work to improve the show.  Want to hear your company in the show? You too can reach the most influential leaders and operators in the industry. Here's our media kit. Contact us at cyberwire@n2k.com to request more info. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © 2023 N2K Networks, Inc.

Shay Davidson is a full-stack web, mobile, and game developer. He is currently leading the front end at Lemonade. The discussion revolves around the use of Supabase as a free database and its comparisons to Firebase for developer experience. They dive into building applications with Next.js and React 18, utilizing React Server Components to interact with the Supabase API. They share their experiences, frustrations, and insights regarding caching mechanisms, server actions, and the challenges of adapting to new technologies in the React ecosystem. The episode also delves into the React server components controversy, the importance of learning and experimenting with new technologies, the use of AI for creative purposes, and the potential dangers of deep fakes.SponsorsChuck's Resume TemplateDeveloper Book ClubBecome a Top 1% Dev with a Top End Devs MembershipSocialsLinkedIn: Shay DavidsonPicksAJ - Dune: Part Two (2024)Dan - Arnold Schwarzenegger Sings About Rainbows (AI)Dan - Finance worker pays out $25 million after video call with deepfake CFOShai - Rendezvous with RamaSupport this podcast at — https://redcircle.com/javascript-jabber/donationsPrivacy & Opt-Out: https://redcircle.com/privacy

Supabase is an open source backend-as-a-service platform and competes directly with Google's Firebase. A key distinction between them is that Firebase is a document store, while Supabase uses Postgres, which is a SQL-based database management system. Software Engineering Daily last covered Supabase in 2020 when its Founder Paul Copplestone came on the show, and a The post Supabase Security with Inian Parameshwaran appeared first on Software Engineering Daily.

In today's episode, we have Bryant, Forrest, and Robert Wensley from Investor Lift. They delve into the current real estate landscape, wholesale deals, and market dynamics. Join Cody and his guests as they explore topics such as The Mobile Marketing Machine, nationwide wholesaling, and how Investor Lift is staying ahead in this evolving industry. Tune in for valuable insights into market conditions, strategic market selection, and our latest features for investor profiles and wholesaling partnerships. They will also address challenges faced by newcomers and highlight the resources provided by platforms like sendustheDeals.com and dodealswithme.com. It's an episode packed with industry wisdom and a touch of humor! Robert Wensley has worked in the real estate industry for the past five years. As the Chief Executive Officer of InvestorLift, a company disrupting the real estate investment industry with data and technology, he has facilitated $1 billion in off-market real estate deals through the software InvestorLift. Additionally, he played a pivotal role in growing multiple wholesaling companies to over $1 million per month in assignment fees earned. Wensley is a Harvard graduate in Economics with minors in Finance and Government, and he holds various certifications, including Firebase, iOS App Development, and Python/Django Full Stack Web Developer.

In this potluck episode of Syntax, Wes and Scott answer your questions about TypeScript just being fancy duct tape, dealing with back pain while coding, rate limits on cloud services, what to use for email provider, is Firebase a legit platform, and more! Show Notes 00:11 Welcome 03:11 The Sunday scaries 06:03 Is TypeSctipt just a bunch of fancy Duck Tape? Is TypeScript saving us? 12:29 How do you go years into programming without back pain? Hasty Treat - Stretching For Developers with Scott — Syntax Podcast 293 23:51 Why don't cloud services provide an option to shut off services when a spending limit is reached? DigitalOcean | Cloud Hosting for Builders Vercel: Develop. Preview. Ship. For the best frontend teams 28:41 How do you choose a CSS library for any project? The most advanced responsive front-end framework in the world. | Foundation 960 Grid System 38:26 What's happening to Level Up Tuts? Level Up Tutorials - Learn modern web development Wheels - Skateboard Wheels - 60mm Cali Roll - Shark Wheel 43:43 Not a sponsored Yeti spot 45:16 What do you do for email hosting? Google Workspace TechSoup Canada Proton Mail: Get a private, secure, and encrypted email account Outlook Microsoft 365 Plans Scheduling Software Everyone Will Love · SavvyCal Synology Photos 50:34 Is Firebase ok to run an app long term with? Firebase 58:57 Am I wrong to not do productive work intensely? 01:34 SIIIIICK ××× PIIIICKS ××× ××× SIIIIICK ××× PIIIICKS ××× Scott: MagSafe Charger, Anker 3-in-1 Cube with MagSafe Wes: 6amLifestyle Headphone Hanger Stand Under Desk Shameless Plugs Scott: Sentry Wes: Wes Bos Tutorials Tweet us your tasty treats Scott's Instagram LevelUpTutorials Instagram Wes' Instagram Wes' Twitter Wes' Facebook Scott's Twitter Make sure to include @SyntaxFM in your tweets Wes Bos on Bluesky Scott on Bluesky Syntax on Bluesky