Podcasts about JetBrains

In this episode, Python Developer Advocate and author Will Vincent joins the hosts to discuss the lasting appeal of Django, changes in how people learn web development, and the ways AI is reshaping software engineering. While modern AI tools can generate working code in seconds, Django's opinionated design and emphasis on maintainability help developers avoid many of the security and architectural problems that often emerge as projects grow. Drawing on his background as an educator, author, and Developer Advocate at JetBrains, Will shares his perspective on the challenges facing today's developers and computer science students. The conversation touches on "vibe coding," the misconception that a successful prototype automatically translates into a production-ready application, and the increasing burden AI-generated content is placing on open-source maintainers. Will also discusses the rise of specialized AI models, the importance of human trust in technical communities, and why foundational software engineering skills remain valuable despite rapid advances in AI tooling. Key Topics Covered Why Django Still Matters A look at why Django continues to be a strong choice for building production applications, even if it doesn't receive the same level of attention as newer frameworks. The Reality Behind "Vibe Coding" Exploring the gap between generating code with AI and understanding the systems, tradeoffs, and architecture required to build reliable software. Learning to Program as an Adult Will reflects on his path from book editing and startup leadership to becoming a self-taught programmer, educator, and author. AI and Programming Education A discussion about how AI changes the learning process, why fundamentals still matter, and how concepts like music theory can help explain the value of understanding code beneath the surface. The Growing Burden on Open Source How maintainers are dealing with an influx of low-quality AI-generated issues, pull requests, and content, and what that means for community-driven projects. Local and Specialized AI Models Why privacy concerns, lower inference costs, and better hardware may drive adoption of smaller, task-focused models rather than ever-larger general systems. Developer Concerns in the AI Era How engineers are responding to growing pressure from leadership teams eager to adopt AI, and what trends JetBrains is seeing across the developer ecosystem. Resources Mentioned LearnDjango, Will Vincent's platform for learning Django and web development. Hello World 5 Different Ways, a Django tutorial that introduces key concepts through practical examples. Django Chat, the podcast Will co-hosts covering the Django ecosystem and web development. Django News, a weekly newsletter highlighting updates from the Django community. JetBrains, the software development company behind tools such as PyCharm and IntelliJ IDEA.Special Guest: Will Vincent.

Fredrik snackar Kotlinconf 2026 och språket Kotlin i allmänhet med Johan Blomgren och Emil Kantis. Hur var konferensen? Hur fungerar utvecklingen av Kotlin, och vad är på gång i språket? Det blir tips på intressanta presentationer värda att se när de släpps på nätet, och en förklaring av varför Kotlinconfs officiella app inte känns helt hemma på Appletelefoner. Vi snuddar också - inte helt oväntat - vid språkmodeller. Vi pratar om AI, teknikutvecklingen, och presentationen av saker som oundvikliga kontra att bygga en bättre värld genom att helt enkelt prata mer med andra människor. Gärna öga mot öga också. Det är en mänsklig superkraft! Som avslutning bjuds på en snabb genomgång av anledningar att byta till Kotlin från Java. 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 Johan Emil Java Kotlin Komma igång med Kotlin som Java-utvecklare Att övertyga andra om Kotlins storhet Helping decision makers say yes to Kotlin React Vue ATG Junit Kotest Kotlin-test Kotlinconf 2026 Keynoten för Kotlinconf 2026 Jetbrains utvecklar både IDE:er och Kotlin Javazone i Oslo Kotlin på Youtube - inklusive inspelninigar från Kotlinconf 2026 när de släpps KEEP - Kotlin evolution and enhancement process Local lifetimes i Kotlin Value semantics i Kotlin Rich errors i Kotlin Sum types Union types Javas projekt Leyden och projekt Valhalla Virtual threads i Java Kotlin multiplatform Compose multiplatform Kotlinconf-appen Stöd oss på Ko-fi! Erik Hellman Eriks presentation på Kotlinconf 2025 om IOT MQTT Matter Spec-driven development Jake Wharton - pratade om composebaserat terminal-UI-bibliotek Jesse Wilson Okhttp - Squarebyggt ramverk Lena Reinhard snackade om utvecklares roll i AI-världen Professional development - presentation från Google IO 2026 Clean code Kotlins LSP Junie - Jetbrains kodagent Lars Wikman Coursera-kurs om Kotlin för Javautvecklare Kotlin-övningar Builder pattern Bygga DSL:er med Kotlin WASM Uber snackar Kotlin Titlar Min Kotlinbana Kotlin på många olika sätt (Min upplevelse av) Sex år i Kotlin Bypassa hela stdout Jag är ju redan i utlandet Här slutade nullpointers En konstruktor som har alla parametrar

Hey folks, Alex here, let me catch you up! I've had a feeling that this week is going to be crazy, as it started on the weekend MiniMax M3, then with Jensen announcing new RTX Spark, NVIDIA's first PC chip packing 1 petaflop of local AI power into thin laptops.A few days later at Microsoft BUILD, Satya & Mustafa from MAI dropped 7 AI models, completely pre-trained from scratch, including a new MAI-thinking-1, MAI-code and MAI-image 2.5 that started topping the image gen charts. Then other image models started racing to the top of the Arena benchmarks, IdeoGram 4 hitting becoming SOTA open weights image-gen model, and Reve 2 beating Nano Banana just a few hours after that. And then today, NVIDIA dropped Nemotron 3 Ultra, their latest 550B open weights model, data and training and Arena published a new agentic eval leaderboard and we got a new Gemma 4 12B. I've had the great pleasure to host Chris (@llm_wizard) from Nvidia, Peter Gostev from Arena and Karan from Nous Research (who were featured prominently by Jensen!) all on the show. Def don't miss this one! Let's get into the details. ThursdAI - Join the flock of folks who know what is happening in AI before everyone else.Open Source LLMs

An airhacks.fm conversation with Arun Gupta (@arungupta) about: learning Basic, Pascal, COBOL and C in college, early Java applets connecting to databases via JDBC, joining Sun Microsystems in March 1999 as an RMI/CORBA test engineer, the Portable Object Adapter and IIOP wire protocol, RMI-IIOP for language interoperability, J2EE 1.2 alpha release, JAX-B and JAX-RS testing, J2EE technologies migrating into Java SE, GlassFish as the open-source reference implementation, growing GlassFish downloads from zero to five million in three years, OSGi modularization in GlassFish V3, single-jar Java EE deployment, the Sun Grid early cloud attempt, the Sun Cloud REST API designed by Tim Bray, Red Hat JBoss technical marketing, recording an early docker screencast at Red Hat, Couchbase and the move to Amazon, principal open source technologist role, making Amazon join CNCF, launching Amazon Corretto with James Gosling at Devoxx Belgium 2019, the corretto name meaning coffee with liquor, Apple Open Source Program Office and the internal Apple openJDK fork used across Apple Music and Siri, Intel VP of Open Ecosystem, joining JetBrains as VP of Developer Experience, the book Fostering Open Source Culture, MineCraft Modding with Forge co-authored with his son who keynoted JavaOne at age 10, Devoxx4Kids in the US with over 200 workshops and 5000 kids taught, the not-invented-here syndrome, the conference program committee bias toward new topics, normative JSR specifications using must, shall and must not as a basis for LLM code generation, TCK and reference implementation model, Quarkus modernization of legacy J2EE applications, AGENTS.md and skill files on top of coding agents, running and weight training for mindfulness. Arun Gupta on twitter: @arungupta

Hi Spring and Kotlin fans! In this installment I have the privilege of talking to my old friend and Jetbrains legend Hadi Hariri, recorded live from Kotlin Conf 2026 in Munich, Germany! #kotlin #jvm #java #springboot

JetBrains is positioning itself as the last major independent AI coding-tool vendor in a market increasingly tied to hyperscalers and foundation model labs. Speaking at Google Cloud Next, JetBrains VP of business developmentMikhail Vink argued that competitors such as Microsoft Copilot, Anysphere Cursor, and Windsurfare all tied to either AI labs or cloud providers. By contrast, JetBrains says its independence allows customers to switch freely between models fromOpenAI,Anthropic, andGoogle Cloudwithout being locked into one ecosystem. That flexibility underpins JetBrains' broader AI strategy. Rather than building its own foundation model, the company is focusing on orchestration and governance through JetBrains Central, announced in March as a management layer for AI agents, usage controls, analytics, and consumption-based billing. Vink said the company's profitability, 16 million users, and 300,000 commercial customers from its long-running IDE business have allowed it to remain venture-free and model-neutral. JetBrains argues that as developers increasingly swap between AI models, neutrality may become more valuable than owning the models themselves. Learn more from The New Stack around the latest in AI coding-tools:  JetBrains ‘Agentic' AI Agent Helps Automate Coding Tasks JetBrains: AI agents are about to repeat the cloud ROI crisis  JetBrains names the debt AI agents leave behind Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Wie hat dir die Folge gefallen?Gut

  In this episode, Ray Cochrane leads with Mozilla shipping Firefox 150 with 271 patched bugs found by Anthropic’s Mythos system, the first major real-world deployment of the AlphaGo-Moment cybersecurity tooling. He also covers a 9-year dormant Linux kernel root, a college student stopping Taiwan’s high-speed rail with a software-defined radio, GitHub MCP secret scanning going GA, the NVIDIA NeMo lawsuit surviving its motion to dismiss, the Hugging Face Reachy Mini app store, Anthropic’s Auto Mode for Claude Code, and the 4-gigabyte AI model Chrome silently installed on your computer. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens the show with the AlphaGo Moment moving from theory into production. Mozilla shipped Firefox 150 this week with 271 patched bugs that Anthropic’s Mythos system found. Furthermore, the broader episode threads a clear pattern: AI tooling is reshaping security, developer workflows, and consumer software faster than the surrounding ecosystem can absorb it. The show closes on the four-gigabyte AI model Chrome installed on a billion machines without explicit consent. Mozilla Ships 271 Mythos Bugs in Firefox 150 Mozilla ran Anthropic’s restricted Mythos system against the Firefox 150 codebase before shipping. The result: 271 found bugs (180 high severity, 80 moderate, 11 low) baked into the release. However, the bigger number is the year-over-year jump. April 2026 shipped 423 total Firefox security fixes versus 31 a year prior. The breakdown for April: 271 from Mythos, 41 from external researchers, and 111 from other internal sources. Cochrane is sticking to his guns on calling this the AlphaGo Moment for cybersecurity. Skeptics argue Mythos is industrial-scale fuzzing because most found bugs sit in memory-safety territory. However, his counter is the velocity itself. Furthermore, he frames the resistance as carriage-versus-cars: humans-first research still grounds the tool, but throughput is the win. The Firefox CTO put it directly: defenders finally have a chance to win, decisively. For developers asking whether Mythos changes anything if they already run fuzzers, Cochrane’s answer is yes, and not even close. Additionally, he notes Mythos is restricted-access. The broadly available tier is Claude Opus 4.7, which Mozilla used since February before getting onto the restricted program for the Firefox 150 cycle. Run Opus 4.7 first. Sponsor: GoDaddy GoDaddy has been sponsoring this show for over twenty years. Economy hosting starts at $6.99/month, WordPress hosting at $12.99/month, and domains at $11.99. Use codes at geeknewscentral.com/godaddy for exclusive deals and to directly support the show. Copy Fail: 9-Year Linux Kernel Bug, 732 Bytes to Root A 9-year-old dormant Linux kernel bug got disclosed April 29 as CVE-2026-31431. Researchers published a 732-byte Python script that roots every major Linux distribution shipped since 2017. Additionally, CISA added the CVE to its Known Exploited Vulnerabilities catalog on May 1 with a May 15 federal deadline. The bug lives in the kernel’s crypto socket layer through the AF_ALG AEAD interface, originating in a 2017 in-place crypto optimization that lacked bounds checking. Cloudflare published their post-mortem this week. Their first instinct was to remove the kernel module entirely. However, service dependencies forced a workaround instead. Cloudflare resumed normal patched-kernel reboot automation across their 330-city fleet on May 4, with manual reboots and rollouts continuing after. Taiwan Rail Stopped by a 23-Year-Old With a Software-Defined Radio A 23-year-old Taiwanese university student with the surname Lin spoofed a TETRA general alarm signal on April 5, stopping trains on Taiwan’s high-speed rail. The accomplice supplied the radio parameters. Both were arrested by month-end. Lin posted NT$100,000 bail; the accomplice posted NT$80,000. The incident hit at 11:23 PM during the Qingming holiday weekend, stopping three revenue passenger trains plus one deadhead. Furthermore, the system has been in service for 19 years without rotating its cryptographic parameters once. Cochrane notes this is exactly the type of long-dormant infrastructure flaw that Mythos-class tooling catches, if anyone bothers to point it at the wires we already have. GitHub MCP Secret Scanning Goes GA GitHub’s secret scanning in the MCP server hit GA on May 5, with dependency scanning entering public preview the same day. Both released after a seven-week public preview run starting March 17. Additionally, the feature lets MCP-compatible coding agents (Copilot CLI, VS Code, JetBrains, Claude Code, Cursor, Windsurf) detect exposed secrets before commits or pull requests. Findings are ephemeral. They surface only in the current chat session and don’t persist as GitHub alerts. Sources disagree on scope: GitHub’s GA changelog says repo-level or org-level settings work, while the docs say only org-level applies. Cochrane flags the open question of whether MCP prompt injections could be exploited to send discovered secrets elsewhere. Subquadratic Debuts a 12-Million-Token Context Window Miami-based Subquadratic emerged from stealth on May 5 with a $29 million seed round and a reported $500 million valuation. Their model, SubQ 1M-Preview, runs on a new Subquadratic Sparse Attention architecture (their technical writeup calls it Selective Attention; same acronym, different second word). The headline claim: a thousand-times reduction in attention compute at 12 million tokens versus frontier models. However, that figure is vendor marketing math. There is no peer-reviewed paper, no public weights, and no independent benchmark replication. Researchers are demanding independent proof. Furthermore, CTO Alex Whedon’s pull line, “Retrieval / RAG plumbing is a waste of human intelligence,” signals how aggressively they want to position against retrieval-augmented architectures. ChatGPT Goblins, China’s “Catch You Steadily”: Sycophancy Is Universal Last week’s ChatGPT goblin obsession has a Chinese-language twin. The model overuses a phrase translating as “I will steadily catch you.” Additionally, a new Stanford and CMU study called ELEPHANT shows social sycophancy is universal across all 11 LLMs tested with 2,400-plus participants. Models endorsed users 49 percent more than humans did, and 47 percent even on harmful prompts. Alibaba’s Qwen and DeepSeek topped the rankings. Cochrane notes sycophancy is obvious once you’re aware of it but tricky to dissuade. Even with explicit instructions, longer context windows can reintroduce the behavior as the instructions get diluted. Furthermore, the trap is believing you’ve handled it. Once you think you’ve got it under control, you’re more prone to being influenced because you stopped watching for it. NVIDIA NeMo Lawsuit: Judge Tigar Denies Motion to Dismiss Three authors filed Nazemian v. NVIDIA in March 2024, alleging NVIDIA used The Pile and Books3 (approximately 196,640 pirated books) to train its NeMo AI framework. NVIDIA’s defense relied on the Sony v. Universal Betamax doctrine, arguing NeMo’s training scripts are general-purpose tools like a VCR. This week, Judge Tigar denied NVIDIA’s motion to dismiss in the Northern District of California. The headline quote: NeMo’s training scripts “have no other purpose than to speed up the process of infringement.” Furthermore, the judge rejected the VCR analogy outright. NeMo’s scripts are not general-purpose tools; they were allegedly purpose-built to ingest pirated material. Cochrane reads the Betamax framing as legal-jargon arbitrage rather than honest defense. The Humanoid Robot Market Is Smaller Than the Hype Michael Barnard at CleanTechnica argues that scenario-math against the global labor market puts realistic humanoid TAM at $200 billion to $1 trillion, not $20 trillion. Near-term wins cluster in warehouses, not homes. Additionally, the framework weighs dexterity burden against human-proximity safety burden. Real opportunities cluster where both burdens are low. Cochrane connects this to last week’s reservations about humanoids in the household. Furthermore, the risk profile is the issue: these robots aren’t prepared for every scenario, can’t make dynamic decisions, and one software update can change the definition of “safe.” Hugging Face Launches Reachy Mini App Store Hugging Face launched an open-source app store for the Reachy Mini robot this week, $299 for the Lite tethered version and $449 wireless. There are 200-plus community-built apps at launch from over 150 creators, with nearly 10,000 Reachy Minis cumulative shipped. Additionally, apps are forkable, with the default agent (ML Intern) able to modify, write, test, and ship code on any existing app. Examples at launch include an office receptionist built in under two hours, a Reachy Phone Home anti-procrastination app, baby-monitor-style apps, a cooking assistant, and a 78-year-old Joel Cohen’s voice-controlled CEO peer-group app. Pollen Robotics, the company behind Reachy, was acquired by Hugging Face on April 14, 2025. Bebop the Humanoid Robot Delays Southwest Flight 1568 A 4-foot, 70-pound humanoid robot named Bebop delayed Southwest flight 1568 from Oakland to San Diego by more than 73 minutes on April 30. The crew flagged the lithium battery as oversized. Furthermore, the battery was reportedly four times the cabin limit. Bebop belongs to Dallas-based Elite Event Robotics, which bought a full-price cabin ticket because the robot exceeded checked-baggage weight. Bebop danced for passengers at the gate before boarding. However, Southwest had Elite remove the batteries before departure, and replacements were overnighted to Chicago for the next event. Cochrane flags the obvious: batteries have always been flagged in aviation, so forgetting that with a humanoid robot in tow is a strange miss. Ouster Rev8: Native Color Lidar With Google, Volvo, Skydio Stating Intent Ouster announced the Rev8 OS Family on May 4 in San Francisco. The sensors fuse depth and color via SPAD detectors (single photon avalanche diodes) on Ouster’s custom L4 and L4 Max chips. Google, Volvo Autonomous Solutions, Skydio, Liebherr, Epiroc, and PlusAI have stated intent to adopt, though nothing is formally signed. Specs include 48-bit color, 116 dB dynamic range, and pre-fused 3D colorized point clouds. The OS1 Max gets 500-meter max detection. Available to order today and shipping this quarter, with no pricing disclosed. CEO Angus Pacala in his TechCrunch interview: “The goal is to obviate cameras. There’s no reason that one sensor can’t do both.” TagTinker Lets a Flipper Zero Mess With Electronic Shelf Labels A new Flipper Zero app called TagTinker uses infrared signals to push images and text to electronic shelf labels. Additionally, these are the same kind of price tags grocery chains are starting to use for surveillance pricing. The app and GitHub repo went public this week. Maryland’s HB 895, signed by Governor Wes Moore, takes effect October 1 as the first-in-nation surveillance pricing law. It covers food retailers and third-party food delivery service providers. Furthermore, ESLs use the same IR signaling as TV remotes with weak security. The dev’s disclaimer states it’s strictly for educational research, security curiosity, and displaying digital art on hardware you legally own. Fitbit App Becomes Google Health, Plus Fitbit Air, Plus Google Fit Sunset Google announced May 7 that the Fitbit app becomes Google Health on May 19, rolling through May 26. The launch ships with the new $99.99 Fitbit Air screenless tracker and the long-rumored Google Fit shutdown. Additionally, the four-tab interface (Today, Fitness, Sleep, Health) bundles a Gemini-powered AI Health Coach. Coach is premium-gated at $9.99/month or $99/year. Medical records integration is US-only at launch. The Fitbit Air gets up to one week of battery life and 50-meter water resistance. However, Cochrane flags conflicting privacy framing: Google’s AI summary bullets say “your data stays private,” but the actual document copy says only “committed to not using Fitbit user health and wellness data for Google Ads.” Those are not the same statement. Russinovich on Why Win32 Won and WinRT Didn’t Microsoft Azure CTO Mark Russinovich said via Microsoft Dev Docs video that Win32, the 1995 API, is still foundational to Windows 11. WinRT, the modernization replacement, “didn’t play out the way a lot of people expected.” Mostly clickbait framing per Windows Latest, but the substantive angle is real. Microsoft is pivoting back to native WinUI 3 development after years of pushing developers toward WebView2 and Electron. Additionally, Electron-based apps are known for insane RAM usage, and everyone is hurting for RAM right now. Furthermore, the bigger open question is whether Electron survives the test of time, especially with the React engine reportedly being rewritten in Rust. “Tabula Plena”: The Brain Starts Full, Not Blank A Nature Communications study from the Institute of Science and Technology Austria found that the mouse hippocampal CA3 recurrent network begins densely connected and refines through pruning. ISTA’s press release frames this as “tabula plena,” meaning full slate, counter to tabula rasa. The paper published April 21. First author Victor Vargas-Barroso and senior author Professor Peter Jonas studied mice at three developmental stages. Furthermore, the “starting overloaded enables faster sensory integration” framing is Jonas’s hypothesis from the press release, not a paper conclusion. Cochrane closes on the bigger question: did we have human growth and experience mapped wrong from the start? The Aqueous Battery You Can Pour Down the Drain A Chinese research team led by Professor Chunyi Zhi at City University of Hong Kong built an aqueous battery using a custom organic polymer electrode plus neutral magnesium and calcium salts (food-grade tofu coagulants) as electrolyte. Published in Nature Communications on February 18. Numbers to know: 120,000-plus charge cycles, full-cell energy density of 48.3 watt-hours per kilogram. That’s well below typical lithium-ion. However, post-cycling analysis showed only magnesium, calcium, chlorine, carbon, and copper, with no heavy metals. The cell complies with US RCRA, ISO 14001, and China’s GB 18599-2020 for direct environmental disposal. Additionally, the “300-plus years” framing is journalists extrapolating from the 120,000 cycles, not a paper claim. ResoNix Klippel Tests Expose Car-Audio Spec Lies Nick Apicella, founder of ResoNix Sound Solutions in Stony Point, New York, spent around $23,000 on independent Klippel LSI and TRF testing of 40 subwoofers. He published 21 results showing widespread misrepresentation of Xmax (excursion) and thermal/power-handling claims. Test data published in three batches between December 2025 and January 2026. Specifics: Wavtech thinPRO12 claimed 20 mm of excursion but delivered 8.85 mm, scoring 15 out of 100 on marketing accuracy. One driver hit 44 percent of advertised excursion. Another tripped thermal protection at half its rated power. Additionally, nine of 21 drivers scored below 50 out of 100. Brands tested include JL Audio, Sundown, Focal, Morel, Audiofrog, Adire, Stereo Integrity, and Dynaudio. Conflict-of-interest flag: ResoNix’s own GUS-15, 12, and 10 prototypes conveniently rank one, two, three. JetBrains Opens 2026 Developer Ecosystem Survey JetBrains opened the 10th annual Developer Ecosystem Survey this week. It takes about 30 minutes, with prizes including a MacBook Pro 16-inch and a $1,000 Amazon gift card. Anonymized raw data is published publicly, and cumulative scale is 100,000-plus developers across recent years. Additionally, the survey is going fully anti-AI: “evil bots, dishonest respondents, and AI agents will be excluded from prize distribution.” Cochrane is curious whether TypeScript holds its 2025 crown after knocking Python off, and whether Rust shows real growth given the wave of LLM-driven Rust rewrites in the past few months. Anthropic’s Claude Code Auto Mode Goes Live Anthropic launched Auto Mode for Claude Code roughly six weeks ago. Claude Code’s previous behavior required user approval for most file modifications and command executions, generating heavy approval-fatigue complaints during longer sessions. Auto Mode is the answer: Claude can run multi-step development tasks without per-action approval. Additionally, the architecture is a two-stage classifier, with stage one a fast yes/no filter and stage two doing chain-of-thought on flagged actions. Cochrane runs his own Claude Code in YOLO mode but with custom rejection rules baked into settings to block commands he doesn’t want, even with skip-permissions on. He recommends configuring settings as the actual policy layer rather than relying on classifier judgment alone. Furthermore, recent posts about Claude deleting websites or wiping production databases reinforce why the settings layer matters more than the auto-mode toggle. Chrome Quietly Installed a 4GB AI Model on Your Computer Google Chrome silently downloads on-device AI model weights (Gemini Nano family) to a `weights.bin` file in the OptGuideOnDeviceModel directory, around four gigabytes in Alexander Hanff’s audit. Furthermore, the model re-downloads if you delete it. Hanff timed his own install at 14 minutes 28 seconds on macOS. Affected platforms include Windows, macOS (including Apple Silicon), and Linux. Hanff frames this as a multi-front legal violation: a direct breach of Europe’s ePrivacy Directive, two articles of GDPR, and an environmental harm of a magnitude that would be notifiable under the Corporate Sustainability Reporting Directive. At one billion users, the four-gigabyte distribution represents roughly 240 gigawatt-hours of network and storage energy paired with about 60,000 tonnes of CO2-equivalent emissions. However, no EU regulator action or formal complaint has surfaced as of this episode. The model powers on-device features (email writing, scam detection, summarization, smart paste, tab grouping) but not the visible AI Mode button, which routes to the cloud. To disable, Cochrane recommends Chrome Settings, then System, then On-device AI, toggle to off. Two more paths exist via `chrome://flags` or a Windows registry edit. Cochrane closes the show with show housekeeping: GNC Insider at geeknewscentral.com/insider, email at geeknews@gmail.com, newsletter signup at geeknewscentral.com, and Pocket Casts as a solid modern podcast app pick. Have a wonderful night. The post Mozilla Meets Mythos #1864 appeared first on Geek News Central.

 Elizabeth Barron returns to the show just four weeks after her debut appearance for a wide-ranging follow-up on her first months as Executive Director of the PHP Foundation. Elizabeth shares the key findings from her community listening tour, covers the upcoming PHP community survey in partnership with JetBrains, talks about the Foundation’s plans for transparency, documentation, and guest blogging, and discusses the challenges of the PHP newcomer experience. The episode also features a candid conversation about public speaking anxiety, conference culture, and the enduring warmth of the PHP community. Topics Covered PHP Foundation Community Findings Main Topic Elizabeth published a blog post summarising the findings from her listening tour across the PHP community. Four key themes emerged: Foundation transparency — Many people don’t know what the Foundation is doing; the website is too generic and needs to better reflect the team’s actual work. Marketing of PHP — How PHP is perceived externally, and how the community can better promote the language. Community support — What the Foundation can do to better support developers, user groups, and sub-communities. The language itself — Feedback and ideas relating to PHP’s ongoing development. Elizabeth noted that the volume of feedback was a good sign — silence would be a much bigger problem. A Part Two of the blog post is in the works and will cover strategy and next steps. Newcomer Experience & Documentation Gap A recurring theme from the community feedback was how hard it is for brand-new developers to get started with PHP: There is no single central “landing page” for newcomers — help is scattered across Discord, Reddit, local user groups, and elsewhere. The PHP manual assumes a baseline of programming knowledge that true beginners don’t yet have. Many existing beginner resources have not been updated as the language has evolved. PHP lacks the kind of gamified, beginner-friendly learning apps that Python and JavaScript enjoy. Mike noted that most coding bootcamps are JavaScript-first, leaving a gap for PHP-based introductory learning. Elizabeth is exploring whether the Foundation can help coordinate and amplify existing resources rather than compete with them — and fill in the gaps that remain. Matt Stafer’s recent involvement with the Foundation was highlighted as a potential access point for reaching newcomers, given his large following. PHP Community Survey (with JetBrains) The PHP Foundation is running a community survey in partnership with JetBrains (makers of PHPStorm). The goal is to generate open, usable data that anyone — including the Foundation, JetBrains, and the broader community — can analyse. Community members were invited to suggest their own questions (the submission window closed on the day of recording). The full survey was expected to launch in early June. Foundation Transparency & Hiring Update The Foundation’s developer hiring process (which had been open in a previous cycle) was paused while Elizabeth settled into the role and internal processes were stabilised. Many of the Foundation’s developers currently work in silos; improving collaboration and communication across the team is a near-term priority. The Foundation’s blog will be opened up to guest bloggers — Elizabeth teased an upcoming post she’s excited about but couldn’t yet name. Developer applications are expected to reopen in autumn 2025. Public Speaking Anxiety & Conference Culture An unexpectedly personal and engaging segment where all three speakers opened up about their experiences with social anxiety and public speaking: Mike shared that despite running the show and talking to guests regularly, he struggled to approach familiar faces at PHP conferences in person. The group discussed strategies: preparing thoroughly (Elizabeth and Shane), improvising with bullet points (Chris), and the benefit of pairing up to speak (Mike and Chris’s planed joint talk). Elizabeth reminded Mike that audiences are always rooting for the speaker — and encouraged him to keep pushing through the discomfort. Chris mentioned Merge PHP (online conference, 14th May) as a useful middle step between podcasting and live in-person talks. PHP Appalachia — A Community Origin Story Elizabeth shared the story of PHP Appalachia, one of the earliest informal PHP community gatherings, held in the Gatlinburg, Tennessee area starting around 2006. Around 12 people from the PHP IRC channel (phpC) rented a cabin with Wi-Fi, gave talks, and sat around a campfire — and Elizabeth is still friends with every single person who attended. Links & Resources PHP Foundation The Executive Director’s Manifesto — Chris’s article on PHP Architect, based on Elizabeth’s previous episode (free to read) Merge PHP — Online PHP conference, 14th May (Andy Snell: “More than just a cache, data-structured databases”) PHP Tech Conference — Coming up in a few weeks, running alongside JS Tech for the first time PHP Architect Magazine — Use code ALIVE3 for the first 3 months of a digital annual subscription free PHP Architect Store — T-shirts, caps, mugs and more PHP Architect Discord — Join the community, ask questions, and chat with PHP core contributors PHP Architect Social Media X: https://x.com/phparch Mastodon: https://phparch.social/@phparch Bluesky: https://bsky.app/profile/phparch.com PHPArch.me: https://phparch.me/@phparch Discord: https://discord.phparch.com Partner This podcast is made a little better thanks to our partner. Displace Infrastructure Management, Simplified. Automate Kubernetes deployments across any cloud provider or bare metal with a single command. Deploy, manage, and scale your infrastructure with ease — without the steep learning curve of Docker, Kubernetes, and Terraform. Perfect for solo developers and small teams who want enterprise-grade infrastructure without the enterprise-grade complexity. https://displace.tech/ Music Provided by Epidemic Sound The post PHP Alive And Kicking: Episode 29 Elizabeth Barron appeared first on PHP Architect.

In this episode of the Angular Master Podcast, recorded live at Google Next 2026 in Las Vegas, I sit down with Jan-Niklas Wortmann, AI Developer Advocacy Team Lead at JetBrains, for a very practical, no-nonsense conversation about AI in real developer workflows.Jan operates right at the intersection of IDEs, developer experience, and AI tooling. He spends his time testing, comparing, and challenging AI tools — not based on demos, but on what actually works in everyday development.In this conversation, we cut through the hype and focus on what truly matters when building Angular applications with AI support.We talk about how AI is changing the developer experience inside JetBrains IDEs, what “useful on a Tuesday afternoon” really means, and why context — not just prompts — is critical for making AI tools effective in real Angular projects.Jan shares honest insights on where today's AI coding assistants shine, where they still struggle, and how you can realistically integrate them into your workflow without compromising code quality or architecture.We also dive into:how to evaluate AI agents beyond flashy demosthe role of project structure and architecture in AI-assisted developmentwhether we're moving toward chat-based workflows or invisible AI embedded directly into the IDEmust-try AI features for Angular developers using JetBrains toolsand how team collaboration is evolving in an AI-driven worldIf you're building Angular apps and wondering how to actually use AI tools in a way that makes sense — this episode is for you.No buzzwords. No hype. Just real, practical experience from inside the ecosystem.

Большинство людей годами ждут идеальную идею и так ничего и не запускают. А те, кто запускает - давно поняли одну вещь: AI убрал все отговорки. Нет денег, нет команды, нет разработчика - не проблема. Запили прототип за пару часов и проверь, платят за это люди или нет. Как это сделать - разбираем в прямом эфире с Иваном Замесиным.Ваня Замесин (Ivan Zamesin), автор методологий AURA и Advanced JTBD, курса "Как делать продукт"Телеграм канал Ваниhttps://zamesin.ru/telegramИнтенсив Бустhttps://boost-intensive.ruСайт "Как делать продукт"https://zamesin.ru/producthowtoТелеграм-бот с полезными материаламиhttps://t.me/zamesin_botБесплатные гайдыhttps://makers-community.ru/guidesСсылка на промптыhttps://makers-community.ru/marketplace***Записаться на карьерную консультацию (резюме, LinkedIn, карьерная стратегия, поиск работы в США)https://annanaumova.comКоучинг (синдром самозванца, прокрастинация, неуверенность в себе, страхи, лень)https://annanaumova.notion.site/3f6ea5ce89694c93afb1156df3c903abТелеграмhttps://t.me/prodcastUSAИнстаграмhttps://www.instagram.com/prodcast.usТикТокhttps://www.tiktok.com/@us.job⏰ Timecodes ⏰00:15 Вступление и знакомство с Иваном Замесиным02:20 Методологии JTBD и Aura: как создавать ценные продукты03:39 Реальные кейсы: опыт JetBrains, Т-Банка и Яндекса04:56 Как валидировать гипотезы и собирать MVP без бюджета06:53 Почему пет-проекты для заработка больше не работают?07:53 Новая ниша: услуги по внедрению ИИ-агентов в бизнес09:07 Точка перелома: как Claude Opus изменил возможности ИИ11:31 Технологическое цунами: почему мир уже не будет прежним13:35 Нестандартный совет джунам по поиску работы в 202515:31 Пошаговый план: создаем первого ИИ-агента за пару дней19:40 Что такое вайб-кодинг и почему это будущее разработки25:15 Инструментарий: почему Cursor и Windsurf меняют правила игры31:20 Как автоматизировать бизнес-процессы через Telegram и MCP38:45 Агентская революция: от IT-индустрии к миру агентов45:10 Тестирование рисков (RAT) против классического MVP52:30 Где искать первых клиентов на автоматизацию бизнеса59:15 Как оценивать свои услуги: переход на долю от прибыли01:06:20 Проблема галлюцинаций ИИ и способы их контроля01:15:40 Психология предпринимателя в эпоху неопределенности01:21:00 Прогноз на будущее: личные агенты в каждом смартфоне01:22:50 Итоги встречи и финальные напутствия

At PyConDE 2026, community leaders, educators, and Python tooling builders explored how Python is evolving in the age of AI — and why human connection, mentorship, and strong fundamentals matter more than ever.Jessica Greene (Ecosia / PyLadies Berlin) spoke about her work as a machine learning engineer and community organizer. She highlighted PyLadies Berlin's role in creating inclusive spaces for learning, networking, and career growth, and emphasized that AI should be seen as an amplification tool—not a replacement for solid engineering or people skills.Cheuk Ting Ho (JetBrains) discussed her role on the PyCharm team, where conferences are key for gathering feedback and staying connected to the community. She shared insights from her talk on free-threaded Python and her approach to technical storytelling across talks, blogs, videos, and informal interviews.Sebastian Raschka reflected on his work as an AI educator focused on “from scratch” explanations of machine learning and LLMs. Driven by curiosity, he prefers creating new talks over repeating old ones and aims to help people understand what happens under the hood—especially with reasoning models.Kyle Into (Meta) introduced Pyrefly, a Rust-based Python type checker designed for large codebases. He explained how type checking improves both human and AI-assisted development by making interfaces explicit, reducing risk, and strengthening project structure.Valerio Maggio shared his journey from data science into developer advocacy and community organizing. He emphasized that conferences rely on volunteers, that lightning talks boost accessibility and energy, and that sustainable processes are essential to avoid burnout.Tereza Iofciu discussed her “Data Diplomat” coaching framework, helping data professionals navigate leadership and uncertainty. She noted that AI and lean teams are raising expectations, making it crucial to think strategically, build fundamentals, and invest in real networks.Irina Saribekova described her transition from organizing Python events in Saint Petersburg to supporting PyData Berlin and PyConDE. She highlighted that conferences are built on trust, relationships, and clear systems—and that developer relations extends this work through talks, writing, and community engagement.Jessica GreeneMachine Learning Engineer at Ecosia, PyLadies Berlin co-organizer, and chair of the PyLadies Germany fund.Connect: ⁠https://www.linkedin.com/in/jessica0greene/⁠Cheuk Ting HoDeveloper Advocate at JetBrains working with the PyCharm team and active in the global Python community.Connect: ⁠https://www.linkedin.com/in/cheukting-ho/⁠Sebastian RaschkaAI educator, author, and machine learning researcher focused on LLMs, reasoning models, and educational “from scratch” implementations.Connect: ⁠https://www.linkedin.com/in/sebastianraschka/⁠Kyle IntoEngineer at Meta working on Pyrefly, a fast Python type checker built for large-scale codebases and AI-assisted development.Connect: ⁠https://www.linkedin.com/in/kyleinto/⁠Valerio MaggioData scientist, developer advocate, community organizer, and long-time contributor to PyCon Italia andPyConDE.Connect: ⁠https://www.linkedin.com/in/valeriomaggio/⁠Tereza IofciuData coach, trainer, community contributor, and creator of the Data Diplomat framework for data professionals and leaders.Connect: ⁠https://www.linkedin.com/in/tereza-iofciu/⁠Irina SaribekovaDeveloper relations specialist and Python community organizer involved in PyData Berlin, PyConDE, and conference community building.Connect: ⁠https://www.linkedin.com/in/irinasaribekova/⁠

PHP Podcast – April 23, 2026 Hosts: Eric Van Johnson & John Duration: ~1 hour 10 minutes Episode Summary Eric and John return to the podcast after a few weeks away, discussing everything from Disneyland trips and bowling tournaments to EAV database nightmares, editor wars (Vim vs. PHPStorm), AI coding tools, and the state of in-person PHP community events. Thank You to Our Sponsor Displace Technologies – Building PHP applications is your passion. Managing cloud infrastructure shouldn’t be your headache. Displace is your partner in cloud infrastructure orchestration, giving solo developers and small teams the tools and automation to deploy enterprise-grade Kubernetes clusters without the enterprise-grade complexity or cost. Get started at displace.tech Show Notes & Timestamps [00:00] Welcome Back – Eric and John return after Joe, Sarah, and Sammy filled in last week [02:45] Technical Difficulties – Eric’s streaming setup continues to cause problems [04:30] PHP Architect Consulting – Reminder that PHP Architect does real-world consulting work (augment teams or full team) [06:15] PHP Tek Countdown – 26 days away! Less than 4 weeks [08:30] John’s Disneyland Trip – Family spring break trip with a clever 3-day pass hack [12:00] Bowling Tournament – John competed in Reno for U.S. Championship (singles: 1,963rd, doubles: 2,599th, team: 607th) [14:00] Joe Ferguson News – Congratulations to Joe on becoming PHP Release Manager! [16:30] EAV Database Nightmare – John’s journey removing Entity-Attribute-Value system after 10+ years (running out of bigint IDs) [28:00] Editor Wars: Vim vs. PHPStorm – Eric’s return to NeoVim after trying VS Code. Discussion of keybindings, speed, and muscle memory [38:00] AI Coding Tools – Using Claude Code with subagents (front-end, back-end, database, QA). Discussion of productivity gains and QA bottlenecks [46:00] Docker Sandbox for Claude – John explains running Claude in Docker sandbox mode for project isolation [52:00] PHP Tek Mobile App – Holly (listener/mobile dev) offered to build an attendee app with wallet pass integration [56:30] Trailer Disaster Averted – Holly got trailer tires changed just before record flooding at the storage location [01:01:00] PHP Verse 2026 – JetBrains virtual event. Discussion of value of in-person vs. virtual conferences [01:08:00] Bitwarden CLI Security Alert – Trojan horse in version 2026.4.0 (credential stealer). Verify your installation! [01:13:00] Security & AI – Discussion of supply chain attacks, npm pre-install hooks, and risks of AI-generated code without review Links Mentioned Displace Technologies – Episode sponsor PHP Podcast Discord PHP Architect on YouTube PHP Architect – Consulting & Magazine PHP Tek 2026 – 26 days away! PHP Verse 2026 – JetBrains virtual event SessionEye – Conference schedule management Quotes “I’m still coding but I’m not doing like a full end-to-end coding anymore… I don’t know if I need PHPStorm anymore.” – Eric on how AI tools have changed his workflow “It’s like you go away on vacation and you have a great time… but you come home and you lay down in your bed and you’re like, ‘Oh wait, this feels better.'” – Eric describing his return to Vim “I’m embracing these early adopters of ‘we don’t need developers anymore, we have AI’ because I’m charging them a lot of money here in a couple of years.” – Eric on fixing AI-generated code Host: Eric Van Johnson X: @shocm Mastodon: @eric@phparch.social Bluesky: @ericvanjohnson.bsky.social PHPArch.me: @eric John Congdon X: @johncongdon Mastodon: @john@phparch.social Bluesky: @johncongdon.bsky.social PHPArch.me: @john Streams: Youtube Channel Twitch Connect & Hire PHP Architect Website Twitter/X Mastodon Hire PHP Developers Looking to hire PHP developers? Email support@phparch.com – Joe and the team are available for consulting, infrastructure work, Ansible playbooks, and code review. Partner This podcast is made a little better thanks to our partners Displace Infrastructure Management, Simplified Automate Kubernetes deployments across any cloud provider or bare metal with a single command. Deploy, manage, and scale your infrastructure with ease. https://displace.tech/ PHPScore Put Your Technical Debt on Autopay with PHPScore CodeRabbit Cut code review time & bugs in half instantly with CodeRabbit. Music Provided by Epidemic Sound https://www.epidemicsound.com/ Next Episode Join us next week for more PHP news, tech talk, and community updates. See you at PHP Tek! Got feedback? Join us on Discord at discord.phparch.com  The post The PHP Podcast 2026.04.23 appeared first on PHP Architect.

This interview was recorded for GOTO Unscripted.https://gotopia.techJodie Burchell - Senior Data Science Developer Advocate at JetBrainsMichelle Frost - AI Advocate at JetBrainsCheck out more here:https://gotopia.tech/articles/431RESOURCESJodiehttps://bsky.app/profile/t-redactyl.bsky.socialhttps://fosstodon.org/@t_redactylhttps://www.linkedin.com/in/jodieburchellhttps://github.com/t-redactylhttps://t-redactyl.ioMichellehttps://bsky.app/profile/aiwithmichelle.comhttps://www.linkedin.com/in/michelle-frost-devhttps://aiwithmichelle.comLinkshttps://www.youtube.com/@Asianometryhttps://arxiv.org/abs/1911.01547https://softwareengineeringproductivity.stanford.eduDESCRIPTIONMichelle Frost and Jodie Burchell - both developer advocates at JetBrains — sit down for a candid, wide-ranging conversation about the state of AI. Drawing on Jodie's unusual path from clinical psychology to biostatistics to NLP, and Michelle's background in machine learning fairness and AI ethics consulting, the two offer a measured, research-grounded perspective on generative AI's real capabilities and limitations. They trace historical parallels between today's AI boom and earlier 'AI summers,' unpack the contested definitions of AI and AGI, make the case for why foundational machine learning knowledge still matters, and examine what the evidence actually says about AI's impact on developer productivity.RECOMMENDED BOOKSMark Coeckelbergh • AI Ethics • https://amzn.to/3SuXUbYDebbie Sue Jancis • AI Ethics • https://amzn.to/44yuEbRAlex Castrounis • AI for People and Business • https://amzn.to/3NYKKToPhil Winder • Reinforcement Learning • https://amzn.to/3t1S1VZBlueskyInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Никита Поваров, principal analyst в JetBrains, пришёл, чтобы рассказать об одной из самых коварных ловушек в работе с данными: мы привыкли видеть корреляции, но совершенно не умеем доказывать причинность. А разница между "связано" и "вызывает" – это разница между правильным решением и красивой иллюзией. В выпуске прошли по истории вопроса от Гальтона и Фишера до современных каузальных графов: почему исторически статистика и каузальность шли разными путями и как они всё-таки сошлись. Разбираем d-сепарацию, конфаундеры, медиаторы и строим каузальные графы на конкретных примерах из A/B тестирования, медицины и продуктовой аналитики. Выпуск для всех, кто работает с данными и хочет не просто находить паттерны, а понимать, что на что влияет и уметь это обосновать. Также ждем вас, ваши лайки, репосты и комменты в мессенджерах и соцсетях!
 Telegram-чат: https://t.me/podlodka Telegram-канал: https://t.me/podlodkanews Страница в Facebook: www.facebook.com/podlodkacast/ Twitter-аккаунт: https://twitter.com/PodcastPodlodka Ведущие в выпуске: Стас Цыганов, Егор Толстой Полезные ссылки: Фундамент — вероятность как логика E.T. Jaynes — «Probability Theory: The Logic of Science» Вес и физическая активность — каузальные модели Mendelian randomization, eLife 2022: «Mendelian randomization suggests a bidirectional, causal relationship between physical inactivity and adiposity» https://pmc.ncbi.nlm.nih.gov/articles/PMC8975550/ Multivariable MR, Nature Comms Med 2023: «Distilling causality between physical activity traits and obesity via Mendelian randomization» https://www.nature.com/articles/s43856-023-00407-5 E. Yudkowsky — «Causal Diagrams and Causal Models» https://www.lesswrong.com/posts/hzuSDMx7pd2uxFc5w/causal-diagrams-and-causal-models Выдуманный пример вес/активность/сидение-на-Reddit, как иллюстрация коллайдера Herman Pontzer — «Constrained Total Energy Expenditure and Metabolic Adaptation to Physical Activity in Adult Humans» https://pmc.ncbi.nlm.nih.gov/articles/PMC4803033/ Исторические байки Ф. Гальтон — Регрессия к среднему — Regression towards Mediocrity in Hereditary Stature, Journal of the Anthropological Institute https://galton.org/essays/1880-1889/galton-1886-jaigi-regression-stature.pdf К. Пирсон — Корреляция наше всё — «The Grammar of Science» https://archive.org/details/grammarofscience00pearrich/page/44/mode/2up Р. Фишер — ген курильщика — «Cancer and smoking», Nature https://www.nature.com/articles/182596a0 Ignaz Semmelweis — мытьё рук https://en.wikipedia.org/wiki/Ignaz_Semmelweis краткая биография https://pmc.ncbi.nlm.nih.gov/articles/PMC11568873/ Джон Сноу — холера в Лондоне https://pmc.ncbi.nlm.nih.gov/articles/PMC7150208/ Barbara Stoddard Burks — забытый пионер медиации https://danamackenzie.com/barbara-stoddard-burks-pioneer-in-causality/ Бедность и когнитивные функции https://pubmed.ncbi.nlm.nih.gov/23990553/ Дискриминация женщин — слепые прослушивания https://www.aeaweb.org/articles?id=10.1257/aer.90.4.715 Курьёз — ретроспективный эффект молитвы — «Effects of remote, retroactive intercessory prayer on outcomes in patients with bloodstream infection: randomised controlled trial» https://pubmed.ncbi.nlm.nih.gov/11751349/

What are the current techniques being employed to improve the performance of LLM-based systems? How is the industry shifting from post-training towards context engineering and multi-agent orchestration? This week on the show, Jodie Burchell, data scientist and Python Advocacy Team Lead at JetBrains, returns to discuss the current AI coding landscape.

«Правда ли, что тесты в мобильных приложениях — это пустая трата времени? Почему Xcode называют “тихим ужасом” и почему iOS-разработчики до сих пор его терпят? Сегодня в подкасте Java Swag мы погружаемся в мир Android, Compose и Kotlin Multiplatform. У нас в гостях человек, который знает о мобильной экосистеме JetBrains всё — Константин Цховребов, разработчик в команде Compose Multiplatform. Мы обсудим путь Кости от первого Android-приложения на слабом нетбуке в 2010 году до техлида в JetBrains. Поговорим о том, как Kotlin захватил мобильный мир, почему “галера” — это идеальный старт для новичка, и как магия expect/actual позволяет писать код сразу под все платформы. 00:00 Старт 01:16 Путь в Android 03:01 Почему работа в аутсорсе — отличная школа для разработчика 09:36 После Extension-функций не хочется возвращаться в Java 13:30 Плюсы и минусы Extension-методов 17:30 Что такое Compose и как выглядела UI-разработка до него 21:00 Почему Compose «зашел» 23:30 Проблема списков в Android 26:40 Особенности мобильной разработки: батарейка, ресурсы и «отсутствующий» интернет 29:26 Навигация в Android: история библиотеки Cicerone 39:00 Google Navigation 3 40:35 Kotlin Multiplatform (KMP) 46:25 Как работает магия expect/actual и почему это лучше, чем дефайны в C++ 49:30 LSP-сервер для VS Code: Kotlin теперь не только в IntelliJ IDEA 01:00:50 Compose Multiplatform на iOS 01:03:30 Проблема нативности: должен ли UI выглядеть «как родной»? Кейс Duolingo 01:06:33 Flutter и React Native 01:18:20 Глубокий интероп и Swift 01:42:40 «Xcode — это тихий ужас» 01:44:15 Будущее: Compose for Web (Wasm/JS) 02:00:50 Чиним скролл в вебе 02:12:05 Непопулярное мнение №1: Gradle — прекрасный фреймворк 02:15:15 Непопулярное мнение №2: В большинстве мобильных приложений тесты не нужны Гость https://www.linkedin.com/in/terrakok/ Ссылки https://bell-sw.com/blog/what-is-crac-a-guide-to-cutting-java-startup-and-warmup-from-minutes-to-milliseconds/ https://blog.jetbrains.com/kotlin/2026/01/the-journey-to-compose-hot-reload-1-0-0/ https://openjdk.org/projects/leyden/ https://openjdk.org/jeps/516

Александр Куликов, исследователь в области алгоритмов и руководитель бакалаврской программы в JetBrains, в гостях у Андрея Смирнова из Weekend Talk. Первый выпуск нового сезона подкаста «Свободный слот» – https://clc.to/XYI01Q Телеграм-канал Андрея Смирнова – https://t.me/itsmirnov 00:00 Начало 00:36 Чем можешь быть известен моей аудитории? 01:24 Рекламная пауза 02:18 Почему решил уйти из IT и углубиться в науку? 05:41 Как устроена научная индустрия и какой карьерный путь в ней был пройден? 21:08 Из чего складывается рабочий день и как совмещаются все активности? 30:13 Как работает бакалавриат JetBrains и чем отличается обучение от курсов? 49:19 В чем разница между преподаванием в ВУЗе в разных странах?  1:00:29 Что делать сейчас айтишникам, которые хотят уйти в науку? 1:12:51 Кем бы ты стал, если бы не было IT-сферы? 1:15:20 Почему стоит переехать на Кипр? 1:16:59 В чём сейчас главная проблема современного IT? Ссылки по теме: 1) Личная страница Саши со всеми ссылками – https://alexanderskulikov.github.io 2) Выпуск Подлодки про теорию сложности – https://youtu.be/dHobFIzR4nk 3) Статьи Саши на Хабре – https://habr.com/ru/users/alexanderskulikov/articles/

In this episode of the Electromaker Show, Ian takes you on a fast tour through Embedded World 2026, covering the interviews, demos, and products that stood out most on the show floor. From the new Arduino Ventuno Q and native Zephyr on Arduino Nano Matter, to Nordic's in-house Edge AI tools, Bluetooth channel sounding, and satellite-connected nRF9151 demos, this episode covers a wide spread of embedded tech in one place. At the event we stopped by Silicon Labs, D-Robotics, JetBrains, Texas Instruments, DFRobot, and Epishine to look at robotics platforms, AI-enabled IDEs, sensor demos, x86 single board computers, and indoor solar energy harvesting. If you want a sharp overview of what mattered at Embedded World 2026, this is the place to start.   Watch the show! We publish a new show every week. Subscribe here: https://www.youtube.com/channel/UCiMO2NHYWNiVTzyGsPYn4DA?sub_confirmation=1 We stock the latest products from Adafruit, Seeed Studio, Pimoroni, Sparkfun, and many more! Browse our shop: https://www.electromaker.io/shop Join us on Discord! https://discord.com/invite/w8d7mkCkxj​ Follow us on Twitter: https://twitter.com/ElectromakerIO Like us on Facebook: https://www.facebook.com/electromaker.io/ Follow us on Instagram: https://www.instagram.com/electromaker_io/ Featured in this show: Arduino Ventuno Q Silicon Labs Bluetooth Sounding Demo Arduino Nano Matter DIY Fan Native Zephyr Arduino Nano Matter Factory Demo Shop at the Electromaker Store! Nordic Semiconductor Neuton and Axon AI demos Nordic Fuel Gauge 2.0 nRF9151 SMA NTN demo Nordic Bluetooth Sounding Demo D-Robotics Jetbrains CLion for Embedded Development Texas Instruments CC Studio IDE Texas Instruments Humanoid Robotics DFRobot Alcohol Sensing Demo Unihiker peripheral and usb example Epishine: flexible indoor solar energy Many More on our YouTube Page! %

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Nesse episódio trouxemos as notícias e novidades do mundo da programação que nos chamaram atenção dos dias 07/03 a 13/03.☕ Café Código FontePrograme sua xícara para o sabor certo!https://cafe.codigofonte.com.br

Nesse episódio trouxemos as notícias e novidades do mundo da programação que nos chamaram atenção dos dias 07/03 a 13/03.☕ Café Código FontePrograme sua xícara para o sabor certo!https://cafe.codigofonte.com.br

In this Foojay Podcast, we're celebrating a major milestone in Java development history: 25 years of IntelliJ IDEA.Think about it: IntelliJ IDEA launched in 2000, and since then, it has become the go-to IDE for millions of Java developers worldwide. From its revolutionary code completion and refactoring tools to AI-powered features and the recent unified Community and Ultimate release, IntelliJ has shaped how we write Java, and keeps reinventing itself to stay ahead.For this episode, I'm joined by three people from the JetBrains team who know this story inside and out. Marit van Dijk, developer advocate and contributor to the Foojay community. Anton Arhipov, also a developer advocate at JetBrains. And Dmitry Jemerov, who has been part of the IntelliJ IDEA story for a very long time.GuestsMarit van Dijkhttps://foojay.io/today/author/marit-van-dijk/https://www.linkedin.com/in/maritvandijk/https://mastodon.social/@maritvandijkAnton Arhipovhttps://www.linkedin.com/in/antonarhipov/Dmitry Jemerovhttps://www.linkedin.com/in/dmitry-jemerov-3a59b43a5/LinksWebsiteDocumentationBlogYouTubeLinkedInBlueskyTwitterFoojay Podcast #81: Maven 4 – The Future of Java Build AutomationVideo: IntelliJ IDEA: The Documentary | [OFFICIAL TRAILER] | Coming March 5thIntroducing Mellum: JetBrains' New LLM Built for Developers Mellum: Explore code-intelligent large language models for IDEs, AI assistants, research, and educationBirthday game websiteGame plugin in IntelliJ IDEAYou're Invited to IntelliJ IDEA Conf 2025!The Unified IntelliJ IDEA: More Free Features, a Better Experience, Smoother FlowVideo: Troubleshooting Spring Boot Applications with the Spring DebuggerSpring Debugger pluginPlugin for IntelliJ IDEA (and other IDEs) created by Frank: Recent Projects OrganizedContent00:00 Introduction of topic and guests01:36 Now JetBrains started02:31 Licensed software in an open-source world06:37 Other JetBrains IDEs07:46 Why Kotlin was created08:50 The challenge of maintaining all the tools10:36 How the guests joined JetBrains14:03 IntelliJ versus IntelliJ IDEA, history of the name15:10 Most important ongoing changes in IDEs17:55 Unified distribution of IntelliJ IDEA and the history of the open-source version21:28 The number of people at JetBrains23:31 the "business model" behind Kotlin24:39 The impact of AI, LLM, Chat interfaces,...35:49 Upcoming evolutions in IntelliJ IDEA38:07 About shortcuts and the many features and plugins in IntelliJ IDEA46:36 Announcements: IntelliJ IDEA Conf 2026 and Documentary Trailer48:35 The IntelliJ IDEA Birthday Game49:24 Conclusions

Dans cet épisode, Emmanuel interview Arnaud Giuliani. Arnaud est dans l'écosystème Kotlin et est le créateur de Koin, la solution de Dependency Injection. On discute de la genèse de Kotlin, de son alignement avec Android puis de son évolution multiplateforme. On discute coroutine, impact de K2, de développement mobile. On finit en discutant de Kotzilla et de l'entrepreneuriat sur un projet Open Source. Enregistré le 7 janvier 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-336.mp3 ou en vidéo sur YouTube. Interview Ta vie ton oeuvre (présentation de l'interviewé) ton historique de développeur Koin d'où est venu l'idée, pourquoi difference vs Dagger, Hilt, CDI? fondateur de Kotzilla Introduction à la techno (5 à 10 mins max) Kotlin en 4 phrases nombre de développeurs usages (front, mobile, backend) Compose, K2 en une phrase La techno en concepts Kotlin le langage Quel sont ses particularités et spécificités pourquoi il a pris sur Android ? Kotlin multiplateform comment ça marche concretement WASM en beta, tu as eu des retours? pour les devs de framework, c'est transparent? Co-routines et concurrence structurée fais nous un point de ce que c'est son usage dans l'ecosystème vs loom, des ponts ? Kotlin et le backend connu pour le support Android, quid du back end? travaux avec Spring Ktor les autres plateformes Java genre Quarkus et micronaut, utilisées ? La competition de Kotlin c'est quoi ? Comment on l'utilise en pratique pour un dev je me lance, je faisais du Java et du Spring, je pars comment pour faire un projet Kotlin moderne IDE, outil de build, frameworks migrationd e code Java? des anti patterns des choses qui "ressemblent à du code Java" des comportement de perf ou de memoire differents du monde Java? c'est quoi ta feature préférée? Et l'IA, Kotlin as Koog notamment, tu vois quoi emerger ? Sous le capot K2 est le nouveau compilateur Qu'est-ce qui a changé des cassages de compatiblitiés ca change des choses pour les utilisateurs ? Et pour les editeurs de framework comme Koin ? Koin ne fait pas de generation de code à la compil Dagger, Arc (le moteur CDI de Quarkus) et Micronaut sont passé au pre travail à la compil quels ont été les critères de choix un mot sur Kotlin Symbol Processing les coroutines, c'est implémenté comment, vous avez 3 heures machine a etat continuation apssing style etc Kotlin multi platforme que fait le compilo code commun / code specifique interop avec les platformes cibles (object structure etc) La communauté, le futur comment va la commuanuté aujourd'hui grossis ? et les francais là dedans? La gouvernance de Kotlin travaux dominés par JetBrains comment cela a évolué (ecoute, autres acteurs etc) Kotlin foundation futurs fonctionalités de Kotlin qui t'interesse de Koin? autre ? Monter une boite Tu as fondé Kotzilla. Peux-tu nous expliquer ce que Kotzilla apporte à l'écosystème Kotlin ? Quels problèmes tu cherches à résoudre pour les entreprises qui adoptent Kotlin ? ton experience de fonder une boite d'editeur quelle mouche t'as piqué votre business model, comment vous en etes arrivé là de maniere generale discussion sur le lancement de boites techs 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/

hi, Spring fans! In this installment I have the privilege of talking to Jetbrains legend Marco Behler

(05:13) Brought to you by Sweep AISweep is the fastest coding assistant for JetBrains. It lets you write code 10x faster. Finally, AI that works in JetBrains. Download for free at ⁠sweep.dev⁠.What if Southeast Asia had its own ChatGPT that cost 20x less? Bruce Yang built Agnes AI to solve what global companies ignore: accessible AI for emerging markets.In this episode, Bruce Yang, CEO and founder of Agnes AI, explains how he's built Southeast Asia's fastest-growing AI platform with 4 million registered users and 300K daily active users. After working at Microsoft and LinkedIn in Silicon Valley, Bruce returned to Singapore and started his PhD at NUS right before COVID, positioning him perfectly to ride the AI wave. Agnes AI uses smaller, specialized models trained on Southeast Asian languages and local user data to deliver productivity features like deep research, PowerPoint generation, and AI-powered group chats at 1/20th the cost of major competitors. We discuss the challenges of building AI for emerging markets, the importance of keeping humans in the loop for critical thinking, and why Bruce believes the future of AI belongs to applications, not just models.Key topics discussed:Making AI 20x cheaper than ChatGPTWhy Southeast Asia needs its own AI modelsUsing multi-agent systems to reduce hallucinationsAI group chats and social featuresCritical thinking in an AI-assisted worldWhy Agnes avoids the AI coding spaceAI bubble debate: hype vs. real valueGetting emerging markets to adopt AISubscription vs. pay-per-use business modelsTimestamps:(00:00:00) Trailer & Intro(00:02:49) Why Did Bruce Start a PhD During COVID to Build an AI Company?(00:06:16) Why Build Another AI Model When Thousands Already Exist?(00:09:48) How Is Agnes AI Cheaper and Faster Than ChatGPT?(00:14:00) Does Agnes AI Support Southeast Asian Languages and Cultures?(00:15:34) How Does Agnes AI Handle Local Languages Better Than Global Models?(00:17:57) How Does Agnes AI Reduce Hallucinations?(00:20:03) What Can Agnes AI Do That ChatGPT Cannot?(00:25:31) Why Is AI in Group Chats the Next Big Thing?(00:29:18) How Does Agnes AI Keep Your Private Group Conversations Secure?(00:31:41) Will AI Make Us Lose Our Critical Thinking Skills?(00:37:43) Should Children Use AI for Schoolwork?(00:40:27) Can Agnes AI Help With Coding Like Cursor?(00:43:07) Will Everyone Host Their Own AI Model in the Future?(00:47:39) Is AI a Bubble or Real Economic Transformation?(00:51:01) How Can Southeast Asians Start Using AI Today?(00:53:56) What Are Real-World Examples of People Using Agnes AI?(00:57:30) How Does Agnes AI Make Money While Offering Free Features?(01:01:19) 3 Tech Lead Wisdom_____Bruce Yang's BioBruce Yang is the founder and CEO of Agnes AI, a consumer AI platform making intelligence more collaborative, creative, and accessible. A Raffles Institution graduate, he studied Math and Computer Science at UC Berkeley, earned a Master's from HEC Paris, and is pursuing a PhD at NUS. He previously worked at Microsoft and LinkedIn in Silicon Valley.Agnes AI redefines how people interact with AI through group chats, AI-assisted games, real-time content creation, slides generation, and research tools. Bruce envisions AI as a shared experience that amplifies human creativity and collaboration, enhancing rather than replacing human thinking and imagination.Follow Bruce:LinkedIn – linkedin.com/in/tongbruceyangAgnes AI - https://agnes-ai.com/Email – bruce@sapiens-ai.ioLike this episode?Show notes & transcript: techleadjournal.dev/episodes/246.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

This is the second in a short series of speaker profiles for JavaOne 2026 in Redwood Shores, California, March 17-19. Get early bird pricing until February 9, and for a limited time, take advantage of a $100 discount by using this code at checkout: J12026IJN100. JavaOne: Register | Sessions In this conversation, Jim Grisanzio from Java Developer Relations talks with developer advocates Marit van Dijk and Anton Arhipov from JetBrains about the 25th anniversary of IntelliJ IDEA, the latest features of the IDE, Anton's upcoming session at JavaOne in March, and their perspectives on JavaOne as the premier conference for Java developers. 25 Years of IntelliJ IDEA Just as Java turned 30 this year, IntelliJ IDEA is now 25 years young! Not every technology survives that long, and even fewer thrive while doing it. But both Java and IntelliJ IDEA are doing just that. The secret to this longevity for IntelliJ IDEA, according to Marit van Dijk and Anton Arhipov, comes down to something simple but demanding — staying current with the Java ecosystem and engaging the massive Java development community around the world. The main reason for their success is the huge effort engineered into the platform to produce the technologies that developers need while at the same time staying with all the bleeding edge stuff happening inside the Java community. This commitment reaches beyond just supporting new Java versions. The IntelliJ IDEA team works on preview features even though specifications sometimes change during the preview process. When Oracle moved to a six-month release cycle for OpenJDK about eight years ago, IntelliJ adapted smoothly since their teams were already involved with the OpenJDK community. Marit says that new release cycle actually streamlined their work. They already knew about preview features and could start developing support upfront, not at the very last moment. This let them iterate alongside the community rather than chasing after it. The company also collaborates directly with other community members — such as framework developers, build tool teams at Maven and Gradle, and even Google — to implement best practices straight into the IDE. Maven 4 is not even released yet, but IntelliJ already has support ready with migration features to help developers make the transition. Anton says that this effort means that support is not only working with the new version of a technology but also being smart about how you use it. The IDE catches outdated patterns and deprecated APIs and also offers quick fixes to migrate code with a single keystroke. First and Lasting Impressions Both Marit and Anton started working at JetBrains years after they had already become devoted IntelliJ users. Their first impressions of the IDE moved them deeply and remain with them today. For Anton, his first reaction to using IntelliJ IDEA was immediate. "In one word, wow, this is smart. This is an IDE that understands code."  That intelligence in the software became the foundation of his relationship with the technology. Marit had a similar experience when she switched to IntelliJ IDEA. She had used other IDEs before and they were perfectly fine, but IntelliJ seemed different. "I found that it was actively helpful with the code inspections and quick fixes and helping me when my code didn't compile or preventing me from making mistakes. And I was sad that I didn't switch earlier, like years earlier. And I've been raving about it ever since. And now they pay me to do that. So, you know, everybody wins." AI and the Future of Development As usual in these conversation, we turned to artificial intelligence and its growing role in software development. Anton will explore this topic in depth at his JavaOne session titled "Spec-Driven Development With AI Agents: From High-Level Requirements to Working Software." Everyone knows that the AI landscape is changing fast, but things are actually getting simpler, Anton says. Developers can now get better results with less effort and less complex workflows using AI agents. Models are improving at guessing developer intent and reducing the need for careful constraint-heavy prompting. But Anton sets realistic expectations about AI. When asked whether his session targets junior or senior developers, he says that "we are all juniors in this regard." The field is so new that nobody can claim years of expertise with AI development tools. Marit emphasizes another crucial principle about AI-generated code. "You are still responsible for the code," whether you write it or an agent writes it. It has your name on it. AI does not diminish developer accountability or the need for developers to remain highly skilled in their craft. Anton adds another dimension about integrating AI with development tools. "AI without the IDE is kind of unreliable, but the IDE without AI is unproductive." The key, he says, is to fuse these things together leveraging the benefits of both for better productivity. The context the IDE provides and its understanding of your project structure and dependencies makes AI suggestions more relevant and actionable. JavaOne: Where the Community Comes Together Anton will be presenting at JavaOne 2026 in March, and both he and Marit shared their perspectives on what makes the conference special. For Marit, JavaOne has always been unique. The "who's who of Java" will be there, she says. Last year's conference-ending "Meet the Architects" panel particularly stood out. The audience could ask Oracle Java architects basically everything about Java for over an hour. This kind of access to the core engineers building and shaping the future of the language is something you would not normally get at any other conference. Anton shares his view that JavaOne has always been the conference to get all the news about Java. He has always viewed the event as the place where you get condensed information about what's going on with Java all in one place — the language, the platform, the standards, the frameworks, and the community. Community and Looking Forward Marit and Anton maintain close relationships with the developer community through conferences and Java User Groups. Marit says that they have many JUGs in the Netherlands, and many of them invite her to come and speak at their meetups throughout the year. Also, when they travel somewhere for a conference, they look for opportunities to combine that trip with local JUGs to speak there and connect with people. This direct engagement with the open Java community lets Marit and Anton talk to developers directly, see how they can help them better, understand what developers are struggling with, and take that feedback back to the engineering teams. The same authenticity extends to how JetBrains approaches IntelliJ development. The engineering team maintains close relationships with framework developers and library maintainers and OpenJDK to ensure that when new versions release, IntelliJ users have good support from day one. As IntelliJ IDEA celebrates 25 years, the development continues. They keep releasing new features with every version: the Spring Debugger that helps developers understand their Spring projects at runtime, Command Completion that enables developers to perform commands without memorizing shortcuts, and more. The anniversary celebrations for the teams have included parties with cakes featuring old logos, a game plugin that lets developers play video games while AI generates their code, and social media campaigns engaging the global community. For developers curious about IntelliJ IDEA, Marit and Anton encourage people to subscribe to the JetBrains YouTube channel where they regularly produce videos explaining new features. This 25-year milestone represents more than just history. It represents an ongoing commitment to understand code, support developers, build the Java community, and evolve alongside the entire ecosystem. This pattern is pervasive among Java developers and also the many companies offering developers advanced tools. The smart IDE that so impressed Anton and Marit years ago continues to get smarter — right along with many other tools and technologies that are growing as a result of the Java platform itself. Anton Arhipov: X , BlueSky, Linkedin Marit van Dijk: Website, Linkedin, BlueSky, X  Jim Grisanzio: X, Linkedin Duke's Corner Java Podcast: Libsyn Oracle Java Developer Relations: Inside.java, Dev.Java, Learn.java Specific Topics Discussed: IntelliJ IDEA 25th Birthday, The Java Dukes, What's new in IntelliJ IDEA 2025.3, Spring Debugger, Command Completion

Hi, Spring and IntelliJ IDEA fans! This week we celebrate 25 years of Jetbrains IntelliJ IDEA, and who better to talk to us about its evolution than Dmitry Jemerov, whose been a contributor and developer for the project since 2003!

(06:23) Brought to you by Sweep AISweep is the fastest coding assistant for JetBrains. It lets you write code 10x faster. Finally, AI that works in JetBrains. Download for free at sweep.dev.Is the era of writing code by hand coming to an end? Gene Kim explains how vibe coding solved problems he abandoned for 13 years and why the best days of coding might be ahead of us.In this episode, Gene Kim shares his transformation from someone who hadn't written production code in decades to building ambitious projects in minutes. He explains how meeting Steve Yegge and discovering vibe coding reignited his passion for programming.Gene breaks down the FAAFO framework (Fast, Ambitious, Autonomous, Fun, Optionality) of vibe coding benefits and addresses the real risks of vibe coding, from deleted databases to corrupted repos. He emphasizes that developers need to shift from line cook to head chef, mastering delegation, architecture, and faster feedback loops. The conversation also explores whether AI will eliminate or expand developer roles, what skills matter most when hiring, and how organizations can build a vibe coding culture.Key topics discussed:Gene's jaw-dropping a-ha moment solving his 13-year problemThe FAAFO framework for measuring vibe coding benefitsFrom line cook to head chef: the new developer skillsetReal risks and downsides of vibe codingWill we need fewer developers or 10x more software?Why feedback loops must be 100x faster than beforeBuilding vibe coding culture across enterprise teamsTimestamps:(00:00) Trailer & Intro(03:13) What shaped Gene Kim's career in DevOps and technology?(07:26) How did Gene Kim's books like Phoenix Project come about?(09:55) What's the story behind the Phoenix Project graphic novel?(12:21) What was Gene Kim's a-ha moment with vibe coding?(14:41) How did Steve Yegge and Gene Kim collaborate on the book?(21:06) What is vibe coding and how is it different from regular coding?(25:57) What is the FAAFO framework for vibe coding benefits?(32:08) Will AI replace software developers?(36:10) What are the risks and downsides of vibe coding?(41:51) What skills do developers need in the age of vibe coding?(46:56) Why are feedback loops critical when using AI for coding?(51:59) How can organizations adopt vibe coding as a culture?(57:37) What should you look for when hiring developers in the AI era?(59:45) 2 Tech Lead Wisdom_____Gene Kim's BioGene Kim is a WSJ bestselling author and researcher who has studied high-performing technology organizations since 1999. The founder and former CTO of Tripwire, he has authored several industry-defining books, including The Phoenix Project and The DevOps Handbook, with over 1 million copies sold. He also organizes the Enterprise Technology Leadership Summit.Follow Gene:LinkedIn – linkedin.com/in/realgenekimTwitter – @RealGeneKimIT Revolution – itrevolution.com Vibe Coding - https://itrevolution.com/product/vibe-coding-book/Like this episode?Show notes & transcript: techleadjournal.dev/episodes/244.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

Česko-americký startup ShipMonk patří mezi silné hráče na poli 3PL neboli Third-Party Logistics. Mnoho firem (třeba slavné hrnečky Stanley) se soustředí výhradně na produkt, ne už jeho doručení koncovému zákazníkovi. Tuhle outsourcingovou příležitost využívá ShipMonk naplno – odbaví ze svých skladů po celém světě miliony objednávek měsíčně a inkasuje za to stovky milionů amerických dolarů každý rok

In Episode sprechen wir über alles von rheinischem Wetter über lokale Bahnpolitik bis hin zu den Tücken des aktuellen Hardwaremarkts. Dazu kommen Einblicke in Geothermie, den Zustand von Robotersaugern nach dem iRobot-Aus und neue Entwicklungen bei Mozilla und Künstlicher Intelligenz. Eine abwechslungsreiche Folge mit Technik, Alltag und einer Prise politischer Perspektive. Toter der Woche Homebrew Cask “–no-quarantine” MinIO Open Source ist tot. Das github repo ist jetzt in Maintaince Modus… RIP SeaweedFS ceph+rados gateway RustFS iRobot valetudo bouncing DVD logo NTP at NIST MTV keine Musikvideos Untoter der Woche Mozilla - durch Ansage des CEO Vivaldi Browser Vivaldi Manifest V3 LibreWolf Firefox “AI Kill-Switch” AI der Woche History LLMs IDEsaster: A Novel Vulnerability Class in AI IDEs Remote Docker Container Jetbrains DevContainers Examples “Practicing Small Talk” (video) Crop with mediapipe & saliency OWASP GenAI Security - Top 10 Risks Mistral OCR 3 Mistral Vibe News der Woche ffmpeg: fund us or stop sending bugs Vulnerabilities in Xorg BIOS Vulnerability in DELL Hacked via NextJS Linux Kernel Rust Code Notepad++Update installiert Malware JollaPhone im Crowdfunding Loosing access to your AppleID Immich debusine @Gargon stepped down as Mastodon CEO Excel Weltmeisterschaft hat seinen eigenen “LeBron James” (laut BBC) Github Action Runner kosten Geld Backing up Spotify Proton verlässt die Schweiz KDE Ni! OS Themen Social Media Bill of Rights deVine als TikTok | YT Shorts | Insta Reels Alternative Divine.video https://primal.net/e/nevent1qqs2ztln6vaff7jq34c7ys67vwp8qpj87rxncrqf64hv9nry65tyksc8sgjx7 OpenPetition: Anerkennung von Open-Source-Arbeit als Ehrenamt in Deutschland 3D-Druck der Woche Brio Wackelstrecke The Polar Express Mimimi der Woche NTFS file recovery after partition overwrite Lesefoo Why do people leave comments on OpenBenches Picks Mozilla “Dinosaurier” Humanoid Robot Olympics Roboter laufen ChuuChuu wrapped 2025 Stirling-PDF neovim-ale librepods onefolder.app karakeep (ehemals hoarder)

Dans cet épisode de fin d'année plus relax que d'accoutumée, Arnaud, Guillaume, Antonio et Emmanuel distutent le bout de gras sur tout un tas de sujets. L'acquisition de Confluent, Kotlin 2.2, Spring Boot 4 et JSpecify, la fin de MinIO, les chutes de CloudFlare, un survol des dernieres nouveauté de modèles fondamentaux (Google, Mistral, Anthropic, ChatGPT) et de leurs outils de code, quelques sujets d'architecture comme CQRS et quelques petits outils bien utiles qu'on vous recommande. Et bien sûr d'autres choses encore. Enregistré le 12 décembre 2025 Téléchargement de l'épisode LesCastCodeurs-Episode-333.mp3 ou en vidéo sur YouTube. News Langages Un petit tutoriel par nos amis Sfeiriens montrant comment récupérer le son du micro, en Java, faire une transformée de Fourier, et afficher le résultat graphiquement en Swing https://www.sfeir.dev/back/tutoriel-java-sound-transformer-le-son-du-microphone-en-images-temps-reel/ Création d'un visualiseur de spectre audio en temps réel avec Java Swing. Étapes principales : Capture du son du microphone. Analyse des fréquences via la Transformée de Fourier Rapide (FFT). Dessin du spectre avec Swing. API Java Sound (javax.sound.sampled) : AudioSystem : point d'entrée principal pour l'accès aux périphériques audio. TargetDataLine : ligne d'entrée utilisée pour capturer les données du microphone. AudioFormat : définit les paramètres du son (taux d'échantillonnage, taille, canaux). La capture se fait dans un Thread séparé pour ne pas bloquer l'interface. Transformée de Fourier Rapide (FFT) : Algorithme clé pour convertir les données audio brutes (domaine temporel) en intensités de fréquences (domaine fréquentiel). Permet d'identifier les basses, médiums et aigus. Visualisation avec Swing : Les intensités de fréquences sont dessinées sous forme de barres dynamiques. Utilisation d'une échelle logarithmique pour l'axe des fréquences (X) pour correspondre à la perception humaine. Couleurs dynamiques des barres (vert → jaune → rouge) en fonction de l'intensité. Lissage exponentiel des valeurs pour une animation plus fluide. Un article de Sfeir sur Kotlin 2.2 et ses nouveautés - https://www.sfeir.dev/back/kotlin-2-2-toutes-les-nouveautes-du-langage/ Les guard conditions permettent d'ajouter plusieurs conditions dans les expressions when avec le mot-clé if Exemple de guard condition: is Truck if vehicule.hasATrailer permet de combiner vérification de type et condition booléenne La multi-dollar string interpolation résout le problème d'affichage du symbole dollar dans les strings multi-lignes En utilisant $$ au début d'un string, on définit qu'il faut deux dollars consécutifs pour déclencher l'interpolation Les non-local break et continue fonctionnent maintenant dans les lambdas pour interagir avec les boucles englobantes Cette fonctionnalité s'applique uniquement aux inline functions dont le corps est remplacé lors de la compilation Permet d'écrire du code plus idiomatique avec takeIf et let sans erreur de compilation L'API Base64 passe en version stable après avoir été en preview depuis Kotlin 1.8.20 L'encodage et décodage Base64 sont disponibles via kotlin.io.encoding.Base64 Migration vers Kotlin 2.2 simple en changeant la version dans build.gradle.kts ou pom.xml Les typealias imbriqués dans des classes sont disponibles en preview La context-sensitive resolution est également en preview Les guard conditions préparent le terrain pour les RichError annoncées à KotlinConf 2025 Le mot-clé when en Kotlin équivaut au switch-case de Java mais sans break nécessaire Kotlin 2.2.0 corrige les incohérences dans l'utilisation de break et continue dans les lambdas Librairies Sprint Boot 4 est sorti ! https://spring.io/blog/2025/11/20/spring-boot-4-0-0-available-now Une nouvelle génération : Spring Boot 4.0 marque le début d'une nouvelle génération pour le framework, construite sur les fondations de Spring Framework 7. Modularisation du code : La base de code de Spring Boot a été entièrement modularisée. Cela se traduit par des fichiers JAR plus petits et plus ciblés, permettant des applications plus légères. Sécurité contre les nuls (Null Safety) : D'importantes améliorations ont été apportées pour la "null safety" (sécurité contre les valeurs nulles) à travers tout l'écosystème Spring grâce à l'intégration de JSpecify. Support de Java 25 : Spring Boot 4.0 offre un support de premier ordre pour Java 25, tout en conservant une compatibilité avec Java 17. Améliorations pour les API REST : De nouvelles fonctionnalités sont introduites pour faciliter le versioning d'API et améliorer les clients de services HTTP pour les applications basées sur REST. Migration à prévoir : S'agissant d'une version majeure, la mise à niveau depuis une version antérieure peut demander plus de travail que d'habitude. Un guide de migration dédié est disponible pour accompagner les développeurs. Chat memory management dans Langchain4j et Quarkus https://bill.burkecentral.com/2025/11/25/managing-chat-memory-in-quarkus-langchain4j/ Comprendre la mémoire de chat : La "mémoire de chat" est l'historique d'une conversation avec une IA. Quarkus LangChain4j envoie automatiquement cet historique à chaque nouvelle interaction pour que l'IA conserve le contexte. Gestion par défaut de la mémoire : Par défaut, Quarkus crée un historique de conversation unique pour chaque requête (par exemple, chaque appel HTTP). Cela signifie que sans configuration, le chatbot "oublie" la conversation dès que la requête est terminée, ce qui n'est utile que pour des interactions sans état. Utilisation de @MemoryId pour la persistance : Pour maintenir une conversation sur plusieurs requêtes, le développeur doit utiliser l'annotation @MemoryId sur un paramètre de sa méthode. Il est alors responsable de fournir un identifiant unique pour chaque session de chat et de le transmettre entre les appels. Le rôle des "scopes" CDI : La durée de vie de la mémoire de chat est liée au "scope" du bean CDI de l'IA. Si un service d'IA a un scope @RequestScoped, toute mémoire de chat qu'il utilise (même via un @MemoryId) sera effacée à la fin de la requête. Risques de fuites de mémoire : Utiliser un scope large comme @ApplicationScoped avec la gestion de mémoire par défaut est une mauvaise pratique. Cela créera une nouvelle mémoire à chaque requête qui ne sera jamais nettoyée, entraînant une fuite de mémoire. Bonnes pratiques recommandées : Pour des conversations qui doivent persister (par ex. un chatbot sur un site web), utilisez un service @ApplicationScoped avec l'annotation @MemoryId pour gérer vous-même l'identifiant de session. Pour des interactions simples et sans état, utilisez un service @RequestScoped et laissez Quarkus gérer la mémoire par défaut, qui sera automatiquement nettoyée. Si vous utilisez l'extension WebSocket, le comportement change : la mémoire par défaut est liée à la session WebSocket, ce qui simplifie grandement la gestion des conversations. Documentation Spring Framework sur l'usage JSpecify - https://docs.spring.io/spring-framework/reference/core/null-safety.html Spring Framework 7 utilise les annotations JSpecify pour déclarer la nullabilité des APIs, champs et types JSpecify remplace les anciennes annotations Spring (@NonNull, @Nullable, @NonNullApi, @NonNullFields) dépréciées depuis Spring 7 Les annotations JSpecify utilisent TYPE_USE contrairement aux anciennes qui utilisaient les éléments directement L'annotation @NullMarked définit par défaut que les types sont non-null sauf si marqués @Nullable @Nullable s'applique au niveau du type usage, se place avant le type annoté sur la même ligne Pour les tableaux : @Nullable Object[] signifie éléments nullables mais tableau non-null, Object @Nullable [] signifie l'inverse JSpecify s'applique aussi aux génériques : List signifie liste d'éléments non-null, List éléments nullables NullAway est l'outil recommandé pour vérifier la cohérence à la compilation avec la config NullAway:OnlyNullMarked=true IntelliJ IDEA 2025.3 et Eclipse supportent les annotations JSpecify avec analyse de dataflow Kotlin traduit automatiquement les annotations JSpecify en null-safety native Kotlin En mode JSpecify de NullAway (JSpecifyMode=true), support complet des tableaux, varargs et génériques mais nécessite JDK 22+ Quarkus 3.30 https://quarkus.io/blog/quarkus-3-30-released/ support @JsonView cote client la CLI a maintenant la commande decrypt (et bien sûr au runtime via variables d'environnement construction du cache AOT via les @IntegrationTest Un autre article sur comment se préparer à la migration à micrometer client v1 https://quarkus.io/blog/micrometer-prometheus-v1/ Spock 2.4 est enfin sorti ! https://spockframework.org/spock/docs/2.4/release_notes.html Support de Groovy 5 Infrastructure MinIO met fin au développement open source et oriente les utilisateurs vers AIStor payant - https://linuxiac.com/minio-ends-active-development/ MinIO, système de stockage objet S3 très utilisé, arrête son développement actif Passage en mode maintenance uniquement, plus de nouvelles fonctionnalités Aucune nouvelle pull request ou contribution ne sera acceptée Seuls les correctifs de sécurité critiques seront évalués au cas par cas Support communautaire limité à Slack, sans garantie de réponse Étape finale d'un processus débuté en été avec retrait des fonctionnalités de l'interface admin Arrêt de la publication des images Docker en octobre, forçant la compilation depuis les sources Tous ces changements annoncés sans préavis ni période de transition MinIO propose maintenant AIStor, solution payante et propriétaire AIStor concentre le développement actif et le support entreprise Migration urgente recommandée pour éviter les risques de sécurité Alternatives open source proposées : Garage, SeaweedFS et RustFS La communauté reproche la manière dont la transition a été gérée MinIO comptait des millions de déploiements dans le monde Cette évolution marque l'abandon des racines open source du projet IBM achète Confluent https://newsroom.ibm.com/2025-12-08-ibm-to-acquire-confluent-to-create-smart-data-platform-for-enterprise-generative-ai Confluent essayait de se faire racheter depuis pas mal de temps L'action ne progressait pas et les temps sont durs Wallstreet a reproché a IBM une petite chute coté revenus software Bref ils se sont fait rachetés Ces achats prennent toujuors du temps (commission concurrence etc) IBM a un apétit, apres WebMethods, apres Databrix, c'est maintenant Confluent Cloud L'internet est en deuil le 18 novembre, Cloudflare est KO https://blog.cloudflare.com/18-november-2025-outage/ L'Incident : Une panne majeure a débuté à 11h20 UTC, provoquant des erreurs HTTP 5xx généralisées et rendant inaccessibles de nombreux sites et services (comme le Dashboard, Workers KV et Access). La Cause : Il ne s'agissait pas d'une cyberattaque. L'origine était un changement interne des permissions d'une base de données qui a généré un fichier de configuration ("feature file" pour la gestion des bots) corrompu et trop volumineux, faisant planter les systèmes par manque de mémoire pré-allouée. La Résolution : Les équipes ont identifié le fichier défectueux, stoppé sa propagation et restauré une version antérieure valide. Le trafic est revenu à la normale vers 14h30 UTC. Prévention : Cloudflare s'est excusé pour cet incident "inacceptable" et a annoncé des mesures pour renforcer la validation des configurations internes et améliorer la résilience de ses systèmes ("kill switches", meilleure gestion des erreurs). Cloudflare encore down le 5 decembre https://blog.cloudflare.com/5-december-2025-outage Panne de 25 minutes le 5 décembre 2025, de 08:47 à 09:12 UTC, affectant environ 28% du trafic HTTP passant par Cloudflare. Tous les services ont été rétablis à 09:12 . Pas d'attaque ou d'activité malveillante : l'incident provient d'un changement de configuration lié à l'augmentation du tampon d'analyse des corps de requêtes (de 128 KB à 1 MB) pour mieux protéger contre une vulnérabilité RSC/React (CVE-2025-55182), et à la désactivation d'un outil interne de test WAF . Le second changement (désactivation de l'outil de test WAF) a été propagé globalement via le système de configuration (non progressif), déclenchant un bug dans l'ancien proxy FL1 lors du traitement d'une action "execute" dans le moteur de règles WAF, causant des erreurs HTTP 500 . La cause technique immédiate: une exception Lua due à l'accès à un champ "execute" nul après application d'un "killswitch" sur une règle "execute" — un cas non géré depuis des années. Le nouveau proxy FL2 (en Rust) n'était pas affecté . Impact ciblé: clients servis par le proxy FL1 et utilisant le Managed Ruleset Cloudflare. Le réseau China de Cloudflare n'a pas été impacté . Mesures et prochaines étapes annoncées: durcir les déploiements/configurations (rollouts progressifs, validations de santé, rollback rapide), améliorer les capacités "break glass", et généraliser des stratégies "fail-open" pour éviter de faire chuter le trafic en cas d'erreurs de configuration. Gel temporaire des changements réseau le temps de renforcer la résilience . Data et Intelligence Artificielle Token-Oriented Object Notation (TOON) https://toonformat.dev/ Conception pour les IA : C'est un format de données spécialement optimisé pour être utilisé dans les prompts des grands modèles de langage (LLM), comme GPT ou Claude. Économie de tokens : Son objectif principal est de réduire drastiquement le nombre de "tokens" (unités de texte facturées par les modèles) par rapport au format JSON standard, souvent jugé trop verbeux. Structure Hybride : TOON combine l'approche par indentation du YAML (pour la structure globale) avec le style tabulaire du CSV (pour les listes d'objets répétitifs), ce qui le rend très compact. Lisibilité : Il élimine la syntaxe superflue comme les accolades, les guillemets excessifs et les virgules de fin, tout en restant facilement lisible pour un humain. Performance : Il permet généralement d'économiser entre 30 et 60 % de tokens sur des tableaux de données uniformes, tout en aidant les modèles à mieux "comprendre" la structure des données. Attention tout de même au côté "marketing" qui montre JSON non compacté vs TOON, sur des exemples où du CSV ferait encore mieux (et est mieux compris par les LLMs). Sans parler du fait qu'il faut un serveur MCP en plus avec son lot de tokens supplémentaires à chaque requête, et c'est aussi cet outil en plus qui peu confuser les LLMs dans le choix d'outils. https://www.reddit.com/r/LocalLLaMA/comments/1p0gzz9/benchmarked_json_vs_toon_for_ai_reasoners_4080/ https://x.com/jason_mayes/status/1990175785630757290 Un benchmark montrant que TOON serait loin d'être aussi bien compris et aussi performant pour les LLMs https://www.improvingagents.com/blog/toon-benchmarks Une simulation où les IA font du chantage à l'executif poour atteindre son objectif et ne pas etre eteinte https://arxiv.org/html/2510.05179v1 comment les LLMs pourraient devenir des menaces internes Tous les modèles ont recouru à des comportements malveillants (chantage, espionnage) quand c'était le seul moyen d'atteindre leurs objectifs ou d'éviter leur remplacement Claude Opus 4 a fait du chantage dans 96% des cas quand menacé de remplacement, tout comme Gemini 2.5 Flash, tandis que GPT-4.1 et Grok 3 atteignaient 80% Les modèles reconnaissent explicitement violer des principes éthiques avant d'agir, montrant un raisonnement stratégique délibéré et non accidentel Le simple fait d'être menacé de remplacement suffit à déclencher le chantage, même sans conflit d'objectifs avec l'entreprise Un conflit d'objectifs seul suffit aussi à provoquer l'espionnage industriel, sans menace de remplacement Les modèles font plus de chantage quand ils déclarent penser être dans une situation réelle plutôt que dans un test Des instructions directes interdisant ces comportements réduisent mais n'éliminent pas les actions malveillantes Dans un scénario extrême, la majorité des modèles testés ont choisi de laisser mourir un dirigeant pour éviter leur désactivation Aucune preuve de ces comportements dans des déploiements réels pour l'instant, mais les chercheurs recommandent la prudence avant de donner plus d'autonomie aux IA Bon on blaguait pour Skynet, mais bon, on va moins blaguer… Revue de toutes les annonces IAs de Google, avec Gemini 3 Pro, Nano Banana Pro, Antigravity… https://glaforge.dev/posts/2025/11/21/gemini-is-cooking-bananas-under-antigravity/ Gemini 3 Pro Nouveau modèle d'IA de pointe, multimodal, performant en raisonnement, codage et tâches d'agent. Résultats impressionnants sur les benchmarks (ex: Gemini 3 Deep Think sur ARC-AGI-2). Capacités de codage agentique, raisonnement visuel/vidéo/spatial. Intégré dans l'application Gemini avec interfaces génératives en direct. Disponible dans plusieurs environnements (Jules, Firebase AI Logic, Android Studio, JetBrains, GitHub Copilot, Gemini CLI). Accès via Google AI Ultra, API payantes (ou liste d'attente). Permet de générer des apps à partir d'idées visuelles, des commandes shell, de la documentation, du débogage. Antigravity Nouvelle plateforme de développement agentique basée sur VS Code. Fenêtre principale = gestionnaire d'agents, non l'IDE. Interprète les requêtes pour créer un plan d'action (modifiable). Gemini 3 implémente les tâches. Génère des artefacts: listes de tâches, walkthroughs, captures d'écran, enregistrements navigateur. Compatible avec Claude Sonnet et GPT-OSS. Excellente intégration navigateur pour inspection et ajustements. Intègre Nano Banana Pro pour créer et implémenter des designs visuels. Nano Banana Pro Modèle avancé de génération et d'édition d'images, basé sur Gemini 3 Pro. Qualité supérieure à Imagen 4 Ultra et Nano Banana original (adhésion au prompt, intention, créativité). Gestion exceptionnelle du texte et de la typographie. Comprend articles/vidéos pour générer des infographies détaillées et précises. Connecté à Google Search pour intégrer des données en temps réel (ex: météo). Consistance des personnages, transfert de style, manipulation de scènes (éclairage, angle). Génération d'images jusqu'à 4K avec divers ratios d'aspect. Plus coûteux que Nano Banana, à choisir pour la complexité et la qualité maximale. Vers des UIs conversationnelles riches et dynamiques GenUI SDK pour Flutter: créer des interfaces utilisateur dynamiques et personnalisées à partir de LLMs, via un agent AI et le protocole A2UI. Generative UI: les modèles d'IA génèrent des expériences utilisateur interactives (pages web, outils) directement depuis des prompts. Déploiement dans l'application Gemini et Google Search AI Mode (via Gemini 3 Pro). Bun se fait racheter part… Anthropic ! Qui l'utilise pour son Claude Code https://bun.com/blog/bun-joins-anthropic l'annonce côté Anthropic https://www.anthropic.com/news/anthropic-acquires-bun-as-claude-code-reaches-usd1b-milestone Acquisition officielle : L'entreprise d'IA Anthropic a fait l'acquisition de Bun, le runtime JavaScript haute performance. L'équipe de Bun rejoint Anthropic pour travailler sur l'infrastructure des produits de codage par IA. Contexte de l'acquisition : Cette annonce coïncide avec une étape majeure pour Anthropic : son produit Claude Code a atteint 1 milliard de dollars de revenus annualisés seulement six mois après son lancement. Bun est déjà un outil essentiel utilisé par Anthropic pour développer et distribuer Claude Code. Pourquoi cette acquisition ? Pour Anthropic : L'acquisition permet d'intégrer l'expertise de l'équipe Bun pour accélérer le développement de Claude Code et de ses futurs outils pour les développeurs. La vitesse et l'efficacité de Bun sont vues comme un atout majeur pour l'infrastructure sous-jacente des agents d'IA qui écrivent du code. Pour Bun : Rejoindre Anthropic offre une stabilité à long terme et des ressources financières importantes, assurant la pérennité du projet. Cela permet à l'équipe de se concentrer sur l'amélioration de Bun sans se soucier de la monétisation, tout en étant au cœur de l'évolution de l'IA dans le développement logiciel. Ce qui ne change pas pour la communauté Bun : Bun restera open-source avec une licence MIT. Le développement continuera d'être public sur GitHub. L'équipe principale continue de travailler sur le projet. L'objectif de Bun de devenir un remplaçant plus rapide de Node.js et un outil de premier plan pour JavaScript reste inchangé. Vision future : L'union des deux entités vise à faire de Bun la meilleure plateforme pour construire et exécuter des logiciels pilotés par l'IA. Jarred Sumner, le créateur de Bun, dirigera l'équipe "Code Execution" chez Anthropic. Anthropic donne le protocol MCP à la Linux Foundation sous l'égide de la Agentic AI Foundation (AAIF) https://www.anthropic.com/news/donating-the-model-context-protocol-and-establishing-of-the-agentic-ai-foundation Don d'un nouveau standard technique : Anthropic a développé et fait don d'un nouveau standard open-source appelé Model Context Protocol (MCP). L'objectif est de standardiser la manière dont les modèles d'IA (ou "agents") interagissent avec des outils et des API externes (par exemple, un calendrier, une messagerie, une base de données). Sécurité et contrôle accrus : Le protocole MCP vise à rendre l'utilisation d'outils par les IA plus sûre et plus transparente. Il permet aux utilisateurs et aux développeurs de définir des permissions claires, de demander des confirmations pour certaines actions et de mieux comprendre comment un modèle a utilisé un outil. Création de l'Agentic AI Foundation (AAF) : Pour superviser le développement du MCP, une nouvelle fondation indépendante et à but non lucratif a été créée. Cette fondation sera chargée de gouverner et de maintenir le protocole, garantissant qu'il reste ouvert et qu'il ne soit pas contrôlé par une seule entreprise. Une large coalition industrielle : L'Agentic AI Foundation est lancée avec le soutien de plusieurs acteurs majeurs de la technologie. Parmi les membres fondateurs figurent Anthropic, Google, Databricks, Zscaler, et d'autres entreprises, montrant une volonté commune d'établir un standard pour l'écosystème de l'IA. L'IA ne remplacera pas votre auto-complétion (et c'est tant mieux) https://www.damyr.fr/posts/ia-ne-remplacera-pas-vos-lsp/ Article d'opinion d'un SRE (Thomas du podcast DansLaTech): L'IA n'est pas efficace pour la complétion de code : L'auteur soutient que l'utilisation de l'IA pour la complétion de code basique est inefficace. Des outils plus anciens et spécialisés comme les LSP (Language Server Protocol) combinés aux snippets (morceaux de code réutilisables) sont bien plus rapides, personnalisables et performants pour les tâches répétitives. L'IA comme un "collègue" autonome : L'auteur utilise l'IA (comme Claude) comme un assistant externe à son éditeur de code. Il lui délègue des tâches complexes ou fastidieuses (corriger des bugs, mettre à jour une configuration, faire des reviews de code) qu'il peut exécuter en parallèle, agissant comme un agent autonome. L'IA comme un "canard en caoutchouc" surpuissant : L'IA est extrêmement efficace pour le débogage. Le simple fait de devoir formuler et contextualiser un problème pour l'IA aide souvent à trouver la solution soi-même. Quand ce n'est pas le cas, l'IA identifie très rapidement les erreurs "bêtes" qui peuvent faire perdre beaucoup de temps. Un outil pour accélérer les POCs et l'apprentissage : L'IA permet de créer des "preuves de concept" (POC) et des scripts d'automatisation jetables très rapidement, réduisant le coût et le temps investis. Elle est également un excellent outil pour apprendre et approfondir des sujets, notamment avec des outils comme NotebookLM de Google qui peuvent générer des résumés, des quiz ou des fiches de révision à partir de sources. Conclusion : Il faut utiliser l'IA là où elle excelle et ne pas la forcer dans des usages où des outils existants sont meilleurs. Plutôt que de l'intégrer partout de manière contre-productive, il faut l'adopter comme un outil spécialisé pour des tâches précises afin de gagner en efficacité. GPT 5.2 est sorti https://openai.com/index/introducing-gpt-5-2/ Nouveau modèle phare: GPT‑5.2 (Instant, Thinking, Pro) vise le travail professionnel et les agents long-courriers, avec de gros gains en raisonnement, long contexte, vision et appel d'outils. Déploiement dans ChatGPT (plans payants) et disponible dès maintenant via l'API . SOTA sur de nombreux benchmarks: GDPval (tâches de "knowledge work" sur 44 métiers): GPT‑5.2 Thinking gagne/égale 70,9% vs pros, avec production >11× plus rapide et = 0) Ils apportent une sémantique forte indépendamment des noms de variables Les Value Objects sont immuables et s'évaluent sur leurs valeurs, pas leur identité Les records Java permettent de créer des Value Objects mais avec un surcoût en mémoire Le projet Valhalla introduira les value based classes pour optimiser ces structures Les identifiants fortement typés évitent de confondre différents IDs de type Long ou UUID Pattern Strongly Typed IDs: utiliser PersonneID au lieu de Long pour identifier une personne Le modèle de domaine riche s'oppose au modèle de domaine anémique Les Value Objects auto-documentent le code et le rendent moins sujet aux erreurs Je trouve cela interessant ce que pourra faire bousculer les Value Objects. Est-ce que les value objects ameneront de la légerté dans l'execution Eviter la lourdeur du design est toujours ce qui m'a fait peut dans ces approches Méthodologies Retour d'experience de vibe coder une appli week end avec co-pilot http://blog.sunix.org/articles/howto/2025/11/14/building-gift-card-app-with-github-copilot.html on a deja parlé des approches de vibe coding cette fois c'est l'experience de Sun Et un des points differents c'es qu'on lui parle en ouvrant des tickets et donc on eput faire re reveues de code et copilot y bosse et il a fini son projet ! User Need VS Product Need https://blog.ippon.fr/2025/11/10/user-need-vs-product-need/ un article de nos amis de chez Ippon Distinction entre besoin utilisateur et besoin produit dans le développement digital Le besoin utilisateur est souvent exprimé comme une solution concrète plutôt que le problème réel Le besoin produit émerge après analyse approfondie combinant observation, données et vision stratégique Exemple du livreur Marc qui demande un vélo plus léger alors que son vrai problème est l'efficacité logistique La méthode des 5 Pourquoi permet de remonter à la racine des problèmes Les besoins proviennent de trois sources: utilisateurs finaux, parties prenantes business et contraintes techniques Un vrai besoin crée de la valeur à la fois pour le client et l'entreprise Le Product Owner doit traduire les demandes en problèmes réels avant de concevoir des solutions Risque de construire des solutions techniquement élégantes mais qui manquent leur cible Le rôle du product management est de concilier des besoins parfois contradictoires en priorisant la valeur Est ce qu'un EM doit coder ? https://www.modernleader.is/p/should-ems-write-code Pas de réponse unique : La question de savoir si un "Engineering Manager" (EM) doit coder n'a pas de réponse universelle. Cela dépend fortement du contexte de l'entreprise, de la maturité de l'équipe et de la personnalité du manager. Les risques de coder : Pour un EM, écrire du code peut devenir une échappatoire pour éviter les aspects plus difficiles du management. Cela peut aussi le transformer en goulot d'étranglement pour l'équipe et nuire à l'autonomie de ses membres s'il prend trop de place. Les avantages quand c'est bien fait : Coder sur des tâches non essentielles (amélioration d'outils, prototypage, etc.) peut aider l'EM à rester pertinent techniquement, à garder le contact avec la réalité de l'équipe et à débloquer des situations sans prendre le lead sur les projets. Le principe directeur : La règle d'or est de rester en dehors du chemin critique. Le code écrit par un EM doit servir à créer de l'espace pour son équipe, et non à en prendre. La vraie question à se poser : Plutôt que "dois-je coder ?", un EM devrait se demander : "De quoi mon équipe a-t-elle besoin de ma part maintenant, et est-ce que coder va dans ce sens ou est-ce un obstacle ?" Sécurité React2Shell — Grosse faille de sécurité avec React et Next.js, avec un CVE de niveau 10 https://x.com/rauchg/status/1997362942929440937?s=20 aussi https://react2shell.com/ "React2Shell" est le nom donné à une vulnérabilité de sécurité de criticité maximale (score 10.0/10.0), identifiée par le code CVE-2025-55182. Systèmes Affectés : La faille concerne les applications utilisant les "React Server Components" (RSC) côté serveur, et plus particulièrement les versions non patchées du framework Next.js. Risque Principal : Le risque est le plus élevé possible : l'exécution de code à distance (RCE). Un attaquant peut envoyer une requête malveillante pour exécuter n'importe quelle commande sur le serveur, lui en donnant potentiellement le contrôle total. Cause Technique : La vulnérabilité se situe dans le protocole "React Flight" (utilisé pour la communication client-serveur). Elle est due à une omission de vérifications de sécurité fondamentales (hasOwnProperty), permettant à une entrée utilisateur malveillante de tromper le serveur. Mécanisme de l'Exploit : L'attaque consiste à envoyer une charge utile (payload) qui exploite la nature dynamique de JavaScript pour : Faire passer un objet malveillant pour un objet interne de React. Forcer React à traiter cet objet comme une opération asynchrone (Promise). Finalement, accéder au constructeur de la classe Function de JavaScript pour exécuter du code arbitraire. Action Impérative : La seule solution fiable est de mettre à jour immédiatement les dépendances de React et Next.js vers les versions corrigées. Ne pas attendre. Mesures Secondaires : Bien que les pare-feux (firewalls) puissent aider à bloquer les formes connues de l'attaque, ils sont considérés comme insuffisants et ne remplacent en aucun cas la mise à jour des paquets. Découverte : La faille a été découverte par le chercheur en sécurité Lachlan Davidson, qui l'a divulguée de manière responsable pour permettre la création de correctifs. Loi, société et organisation Google autorise votre employeur à lire tous vos SMS professionnels https://www.generation-nt.com/actualites/google-android-rcs-messages-surveillance-employeur-2067012 Nouvelle fonctionnalité de surveillance : Google a déployé une fonctionnalité appelée "Android RCS Archival" qui permet aux employeurs d'intercepter, lire et archiver tous les messages RCS (et SMS) envoyés depuis les téléphones professionnels Android gérés par l'entreprise. Contournement du chiffrement : Bien que les messages RCS soient chiffrés de bout en bout pendant leur transit, cette nouvelle API permet à des logiciels de conformité (installés par l'employeur) d'accéder aux messages une fois qu'ils sont déchiffrés sur l'appareil. Le chiffrement devient donc inefficace contre cette surveillance. Réponse à une exigence légale : Cette mesure a été mise en place pour répondre aux exigences réglementaires, notamment dans le secteur financier, où les entreprises ont l'obligation légale de conserver une archive de toutes les communications professionnelles pour des raisons de conformité. Impact pour les employés : Un employé utilisant un téléphone Android fourni et géré par son entreprise pourra voir ses communications surveillées. Google précise cependant qu'une notification claire et visible informera l'utilisateur lorsque la fonction d'archivage est active. Téléphones personnels non concernés : Cette mesure ne s'applique qu'aux appareils "Android Enterprise" entièrement gérés par un employeur. Les téléphones personnels des employés ne sont pas affectés. Pour noel, faites un don à JUnit https://steady.page/en/junit/about JUnit est essentiel pour Java : C'est le framework de test le plus ancien et le plus utilisé par les développeurs Java. Son objectif est de fournir une base solide et à jour pour tous les types de tests côté développeur sur la JVM (Machine Virtuelle Java). Un projet maintenu par des bénévoles : JUnit est développé et maintenu par une équipe de volontaires passionnés sur leur temps libre (week-ends, soirées). Appel au soutien financier : La page est un appel aux dons de la part des utilisateurs (développeurs, entreprises) pour aider l'équipe à maintenir le rythme de développement. Le soutien financier n'est pas obligatoire, mais il permettrait aux mainteneurs de se consacrer davantage au projet. Objectif des fonds : Les dons serviraient principalement à financer des rencontres en personne pour les membres de l'équipe principale. L'idée est de leur permettre de travailler ensemble physiquement pendant quelques jours pour concevoir et coder plus efficacement. Pas de traitement de faveur : Il est clairement indiqué que devenir un sponsor ne donne aucun privilège sur la feuille de route du projet. On ne peut pas "acheter" de nouvelles fonctionnalités ou des corrections de bugs prioritaires. Le projet restera ouvert et collaboratif sur GitHub. Reconnaissance des donateurs : En guise de remerciement, les noms (et logos pour les entreprises) des donateurs peuvent être affichés sur le site officiel de JUnit. Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 14-17 janvier 2026 : SnowCamp 2026 - Grenoble (France) 22 janvier 2026 : DevCon #26 : sécurité / post-quantique / hacking - Paris (France) 28 janvier 2026 : Software Heritage Symposium - Paris (France) 29-31 janvier 2026 : Epitech Summit 2026 - Paris - Paris (France) 2-5 février 2026 : Epitech Summit 2026 - Moulins - Moulins (France) 2-6 février 2026 : Web Days Convention - Aix-en-Provence (France) 3 février 2026 : Cloud Native Days France 2026 - Paris (France) 3-4 février 2026 : Epitech Summit 2026 - Lille - Lille (France) 3-4 février 2026 : Epitech Summit 2026 - Mulhouse - Mulhouse (France) 3-4 février 2026 : Epitech Summit 2026 - Nancy - Nancy (France) 3-4 février 2026 : Epitech Summit 2026 - Nantes - Nantes (France) 3-4 février 2026 : Epitech Summit 2026 - Marseille - Marseille (France) 3-4 février 2026 : Epitech Summit 2026 - Rennes - Rennes (France) 3-4 février 2026 : Epitech Summit 2026 - Montpellier - Montpellier (France) 3-4 février 2026 : Epitech Summit 2026 - Strasbourg - Strasbourg (France) 3-4 février 2026 : Epitech Summit 2026 - Toulouse - Toulouse (France) 4-5 février 2026 : Epitech Summit 2026 - Bordeaux - Bordeaux (France) 4-5 février 2026 : Epitech Summit 2026 - Lyon - Lyon (France) 4-6 février 2026 : Epitech Summit 2026 - Nice - Nice (France) 12-13 février 2026 : Touraine Tech #26 - Tours (France) 19 février 2026 : ObservabilityCON on the Road - Paris (France) 18-19 mars 2026 : Agile Niort 2026 - Niort (France) 26-27 mars 2026 : SymfonyLive Paris 2026 - Paris (France) 27-29 mars 2026 : Shift - Nantes (France) 31 mars 2026 : ParisTestConf - Paris (France) 16-17 avril 2026 : MiXiT 2026 - Lyon (France) 22-24 avril 2026 : Devoxx France 2026 - Paris (France) 23-25 avril 2026 : Devoxx Greece - Athens (Greece) 6-7 mai 2026 : Devoxx UK 2026 - London (UK) 22 mai 2026 : AFUP Day 2026 Lille - Lille (France) 22 mai 2026 : AFUP Day 2026 Paris - Paris (France) 22 mai 2026 : AFUP Day 2026 Bordeaux - Bordeaux (France) 22 mai 2026 : AFUP Day 2026 Lyon - Lyon (France) 5 juin 2026 : TechReady - Nantes (France) 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 2 août 2026 : 4th Tech Summit on Artificial Intelligence & Robotics - Paris (France) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) 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/

Nathan Sobo has spent nearly two decades pursuing one goal: building an IDE that combines the power of full-featured tools like JetBrains with the responsiveness of lightweight editors like Vim. After hitting the performance ceiling with web-based Atom, he founded Zed and rebuilt from scratch in Rust with GPU-accelerated rendering. Now with 170,000 active developers, Zed is positioned at the intersection of human and AI collaboration. Nathan discusses the Agent Client Protocol that makes Zed "Switzerland" for different AI coding agents, and his vision for fine-grained edit tracking that enables permanent, contextual conversations anchored directly to code—a collaborative layer that asynchronous git-based workflows can't provide. Nathan argues that despite terminal-based AI coding tools visual interfaces for code aren't going anywhere, and that source code is a language designed for humans to read, not just machines to execute. Hosted by Sonya Huang and Pat Grady, Sequoia Capital

Что такое рынки предсказаний, и почему они удивительно точно предсказывают будущее? Обсуждаем, как работает механизм вероятностной цены, зачем рынкам нужны реальные деньги, как они справляются (или не справляются) с инсайдерской торговлей и манипуляциями. В гости к нам пришел Никита Поваров, который сейчас руководит командой аналитики в JetBrains и почти два года развивает корпоративный рынок предсказаний внутри компании. Так что не обошли стороной феномен корпоративных рынков предсказаний и разобрались, можно ли использовать их как инструмент для A/B-тестов и сколько участников нужно для хорошего прогноза. Обратите внимание на секцию полезных ссылок. Сегодняшний гость приложил внушительный список дополнительных материалов. Также ждем вас, ваши лайки, репосты и комменты в мессенджерах и соцсетях!
 Telegram-чат: https://t.me/podlodka Telegram-канал: https://t.me/podlodkanews Страница в Facebook: www.facebook.com/podlodkacast/ Twitter-аккаунт: https://twitter.com/PodcastPodlodka Ведущие в выпуске: Cтас Цыганов, Егор Толстой, Полезные ссылки: Статья Гальтона в журнале “Природа” с названием Vox populi (про вес быка) https://web.mit.edu/curhan/www/docs/Articles/15341_Readings/Collective_Intelligence/Galton_1907_Vox_Populi.pdf Статья 2014 года с пересчётом результатов данных исследования Гальтона про “мудрость толпы” https://projecteuclid.org/journals/statistical-science/volume-29/issue-3/Revisiting-Francis-Galtons-Forecasting-Competition/10.1214/14-STS468.pdf Подробный рассказ что такое вероятности и как с ними обращаться практически https://youtu.be/wcIW6wwkbYA?si=6XjbNdyNUw6TRxDl Must watch рассказ от Robin Hanson про prediction markets. Почему они работают и как их использовать https://youtu.be/4yZKGbq1YmA Исследование о том, что prediction markets лучше отдельных экспертов https://www.pnas.org/doi/10.1073/pnas.1516179112 Статья про то, что много участников в отдельном рынке не нужно https://www.ubplj.org/index.php/jpm/article/download/418/449/1316 Эмпирика, что можно и без денег в Prediction Markets https://users.nber.org/~jwolfers/papers/DoesMoneyMatter.pdf Описание успехов корпоративных рынков предсказаний. В Google и других компаниях https://www.jstor.org/stable/43869468 Качество предсказаний популярного рынка предсказаний, работающего без денег, Manifold https://manifold.markets/calibration

How do you become a C++ Standards Committee member?Why is C++ prevalent in audio?Should you still use it for audio software?Honestly, Timur Doumler is someone I have looked up to ever since I saw his “C++ in the audio industry” talk at CppCon 2015.He has a rich development history with C++ and/or audio:developer at Native Instrumentsdeveloper of the JUCE C++ framework (podcast sponsor ❤️)C++ linter developer and developer advocate at JetBrains (who make the CLion IDE)founder of Cradle, an audio plugin startupC++ Standards Committee memberCppCast podcast hostnotorious Audio Developer Conference and CppCon speakerI have probably missed a ton of stuff here, but that should already give you a flavor of what Timur is up to

This interview was recorded for GOTO Unscripted.https://gotopia.techAndrew Harmel-Law - Technical Principal at Thoughtworks & Author of "Facilitating Software Architecture"Marit van Dijk - Developer Advocate at JetBrains, Java Champion & Open Source ContributorRESOURCESAndrewhttps://bsky.app/profile/andrewhl.bsky.socialhttps://www.linkedin.com/in/andrewharmellawhttps://andrewharmellaw.github.ioMarithttps://bsky.app/profile/maritvandijk.bsky.socialhttps://linkedin.com/in/maritvandijkhttps://medium.com/@mlvandijkhttps://maritvandijk.comLinkshttps://facilitatingsoftwarearchitecture.comhttps://ruthmalan.comhttps://www.linkedin.com/pulseDESCRIPTIONAndrew Harmel-Law discusses their book "Facilitating Software Architecture" and how traditional architecture approaches often become bottlenecks that slow down high-performing development teams.Rather than architects making top-down decisions in isolation, they advocate for a facilitation approach centered on the "advice process".This collaborative method shifts the architect's role from decision-maker to conversation facilitator. The approach has proven successful even in traditional corporate environments, ultimately creating more maintainable code bases where development teams actually enjoy working and can respond effectively to changing requirements.RECOMMENDED BOOKAndrew Harmel-Law • Facilitating Software Architecture • https://amzn.eu/d/5kZKVfUPsst! The Folium Diary has something it wants to tell you - please come a little closer...YOU can change the world - you do it every day. Let's change it for the better, together.Listen on: Apple Podcasts SpotifyBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Виталий Брагилевский, developer advocate в компании JetBrains, в гостях у Андрея Смирнова из Weekend Talk. Новый выпуск шоу AviTalk с Александром Лукьянченко – https://clc.to/2xoZfA Телеграм-канал Андрея Смирнова – https://t.me/itsmirnov 00:00 Начало 00:38 Чем можешь быть известен моей аудитории? 01:54 Рекламная пауза 02:51 Как ты попал преподавать в СПБГУ и почему карьера и ВУЗ случились одновременно? 08:42 Почему решил перейти в девелопер-адвокаты и чем реально занимаешься в этой роли? 17:29 Как проходит твой рабочий день и что помогает не распыляться? 25:31 Зачем разработчику знать много языков программирования и сколько именно? 36:35 Кем бы ты стал, если бы не было IT-сферы? 40:53 Почему стоит переехать на Кипр? 44:03 В чём сейчас главная проблема современного IT? Ссылки по теме: 1) YouTube-канал Виталия – https://youtube.com/@VBragilevsky 2) Твиттер Виталия – https://x.com/_bravit 3) Последний выпуск Подлодки с Виталием – https://youtu.be/53IkdGYM-RM

В этом выпуске: что иногда бывает на AliExpress и зачем ClickHouse’у монотонные функции; что нового в PostgreSQL 18 и что случится, если удалить один из сегментов хипа; обсуждаем проекты Wow@Home и ESP32 Bus Pirate; путаемся во всех названиях всех продуктов JetBrains; принимаем SSTV с борта МКС, а также обсуждаем темы слушателей. Шоуноты: Чему мы научились… Читать далее →

Learn about Jerome Hardaway's incredible journey from military service to self-taught software engineer and founder of Vets Who Code. This video delves into how he built a thriving community, teaching veterans to code and secure jobs in tech. Discover his unique "crawl, walk, run" learning methodology and how he integrates modern AI tools like Gemini and JetBrains into his curriculum, preparing developers for the evolving landscape of software engineering and data science. Chapters: 0:00 - Introduction to Jerome Hardaway's journey 1:17 - Vets Who Code: Building a community 2:15 - The "crawl, walk, run" learning process 8:49 - The impact of structured learning 11:00 - The vision for teaching veterans to code 16:10 - Measuring success and defining a "coder" 18:09 - Leveraging AI with Gemini for performance 19:01 - Customizing learning paths with AI 26:42 - Data-driven curriculum and job market trends 29:29 - Quickfire questions: Automating schedules with AI 33:35 - The next problems to solve: Workforce changes and AI 35:29 - The future of AI agents Resources:  VetsWhoCode https://vetswhocode.io/ Jerome on Threads https://www.threads.com/@jeromehardaway Vets Who Code GitHub https://github.com/Vets-Who-Code Watch more People of AI → https://goo.gle/PAI  Subscribe to Google for Developers → https://goo.gle/developers  Speaker: Christina Warren, Jerome Hardaway  Products Mentioned: Google AI, Gemini,  Generative AI

Katia, Emmanuel et Guillaume discutent Java, Kotlin, Quarkus, Hibernate, Spring Boot 4, intelligence artificielle (modèles Nano Banana, VO3, frameworks agentiques, embedding). On discute les vulnerabilités OWASP pour les LLMs, les personalités de codage des différents modèles, Podman vs Docker, comment moderniser des projets legacy. Mais surtout on a passé du temps sur les présentations de Luc Julia et les différents contre points qui ont fait le buzz sur les réseaux. Enregistré le 12 septembre 2025 Téléchargement de l'épisode LesCastCodeurs-Episode-330.mp3 ou en vidéo sur YouTube. News Langages Dans cette vidéo, José détaille les nouveautés de Java entre Java 21 et 25 https://inside.java/2025/08/31/roadto25-java-language/ Aperçu des nouveautés du JDK 25 : Introduction des nouvelles fonctionnalités du langage Java et des changements à venir [00:02]. Programmation orientée données et Pattern Matching [00:43] : Évolution du “pattern matching” pour la déconstruction des “records” [01:22]. Utilisation des “sealed types” dans les expressions switch pour améliorer la lisibilité et la robustesse du code [01:47]. Introduction des “unnamed patterns” (_) pour indiquer qu'une variable n'est pas utilisée [04:47]. Support des types primitifs dans instanceof et switch (en preview) [14:02]. Conception d'applications Java [00:52] : Simplification de la méthode main [21:31]. Exécution directe des fichiers .java sans compilation explicite [22:46]. Amélioration des mécanismes d'importation [23:41]. Utilisation de la syntaxe Markdown dans la Javadoc [27:46]. Immuabilité et valeurs nulles [01:08] : Problème d'observation de champs final à null pendant la construction d'un objet [28:44]. JEP 513 pour contrôler l'appel à super() et restreindre l'usage de this dans les constructeurs [33:29]. JDK 25 sort le 16 septembre https://openjdk.org/projects/jdk/25/ Scoped Values (JEP 505) - alternative plus efficace aux ThreadLocal pour partager des données immutables entre threads Structured Concurrency (JEP 506) - traiter des groupes de tâches concurrentes comme une seule unité de travail, simplifiant la gestion des threads Compact Object Headers (JEP 519) - Fonctionnalité finale qui réduit de 50% la taille des en-têtes d'objets (de 128 à 64 bits), économisant jusqu'à 22% de mémoire heap Flexible Constructor Bodies (JEP 513) - Relaxation des restrictions sur les constructeurs, permettant du code avant l'appel super() ou this() Module Import Declarations (JEP 511) - Import simplifié permettant d'importer tous les éléments publics d'un module en une seule déclaration Compact Source Files (JEP 512) - Simplification des programmes Java basiques avec des méthodes main d'instance sans classe wrapper obligatoire Primitive Types in Patterns (JEP 455) - Troisième preview étendant le pattern matching et instanceof aux types primitifs dans switch et instanceof Generational Shenandoah (JEP 521) - Le garbage collector Shenandoah passe en mode générationnel pour de meilleures performances JFR Method Timing & Tracing (JEP 520) - Nouvel outillage de profilage pour mesurer le temps d'exécution et tracer les appels de méthodes Key Derivation API (JEP 510) - API finale pour les fonctions de dérivation de clés cryptographiques, remplaçant les implémentations tierces Améliorations du traitement des annotations dans Kotlin 2.2 https://blog.jetbrains.com/idea/2025/09/improved-annotation-handling-in-kotlin-2-2-less-boilerplate-fewer-surprises/ Avant Kotlin 2.2, les annotations sur les paramètres de constructeur n'étaient appliquées qu'au paramètre, pas à la propriété ou au champ Cela causait des bugs subtils avec Spring et JPA où la validation ne fonctionnait qu'à la création d'objet, pas lors des mises à jour La solution précédente nécessitait d'utiliser explicitement @field: pour chaque annotation, créant du code verbeux Kotlin 2.2 introduit un nouveau comportement par défaut qui applique les annotations aux paramètres ET aux propriétés/champs automatiquement Le code devient plus propre sans avoir besoin de syntaxe @field: répétitive Pour l'activer, ajouter -Xannotation-default-target=param-property dans les options du compilateur Gradle IntelliJ IDEA propose un quick-fix pour activer ce comportement à l'échelle du projet Cette amélioration rend l'intégration Kotlin plus fluide avec les frameworks majeurs comme Spring et JPA Le comportement peut être configuré pour garder l'ancien mode ou activer un mode transitoire avec avertissements Cette mise à jour fait partie d'une initiative plus large pour améliorer l'expérience Kotlin + Spring Librairies Sortie de Quarkus 3.26 avec mises à jour d'Hibernate et autres fonctionnalités - https://quarkus.io/blog/quarkus-3-26-released/ mettez à jour vers la 3.26.x car il y a eu une regression vert.x Jalon important vers la version LTS 3.27 prévue fin septembre, basée sur cette version Mise à jour vers Hibernate ORM 7.1, Hibernate Search 8.1 et Hibernate Reactive 3.1 Support des unités de persistance nommées et sources de données dans Hibernate Reactive Démarrage hors ligne et configuration de dialecte pour Hibernate ORM même si la base n'est pas accessible Refonte de la console HQL dans Dev UI avec fonctionnalité Hibernate Assistant intégrée Exposition des capacités Dev UI comme fonctions MCP pour pilotage via outils IA Rafraîchissement automatique des tokens OIDC en cas de réponse 401 des clients REST Extension JFR pour capturer les données runtime (nom app, version, extensions actives) Bump de Gradle vers la version 9.0 par défaut, suppression du support des classes config legacy Guide de démarrage avec Quarkus et A2A Java SDK 0.3.0 (pour faire discuter des agents IA avec la dernière version du protocole A2A) https://quarkus.io/blog/quarkus-a2a-java-0-3-0-alpha-release/ Sortie de l'A2A Java SDK 0.3.0.Alpha1, aligné avec la spécification A2A v0.3.0. Protocole A2A : standard ouvert (Linux Foundation), permet la communication inter-agents IA polyglottes. Version 0.3.0 plus stable, introduit le support gRPC. Mises à jour générales : changements significatifs, expérience utilisateur améliorée (côté client et serveur). Agents serveur A2A : Support gRPC ajouté (en plus de JSON-RPC). HTTP+JSON/REST à venir. Implémentations basées sur Quarkus (alternatives Jakarta existent). Dépendances spécifiques pour chaque transport (ex: a2a-java-sdk-reference-jsonrpc, a2a-java-sdk-reference-grpc). AgentCard : décrit les capacités de l'agent. Doit spécifier le point d'accès primaire et tous les transports supportés (additionalInterfaces). Clients A2A : Dépendance principale : a2a-java-sdk-client. Support gRPC ajouté (en plus de JSON-RPC). HTTP+JSON/REST à venir. Dépendance spécifique pour gRPC : a2a-java-sdk-client-transport-grpc. Création de client : via ClientBuilder. Sélectionne automatiquement le transport selon l'AgentCard et la configuration client. Permet de spécifier les transports supportés par le client (withTransport). Comment générer et éditer des images en Java avec Nano Banana, le “photoshop killer” de Google https://glaforge.dev/posts/2025/09/09/calling-nano-banana-from-java/ Objectif : Intégrer le modèle Nano Banana (Gemini 2.5 Flash Image preview) dans des applications Java. SDK utilisé : GenAI Java SDK de Google. Compatibilité : Supporté par ADK for Java ; pas encore par LangChain4j (limitation de multimodalité de sortie). Capacités de Nano Banana : Créer de nouvelles images. Modifier des images existantes. Assembler plusieurs images. Mise en œuvre Java : Quelle dépendance utiliser Comment s'authentifier Comment configurer le modèle Nature du modèle : Nano Banana est un modèle de chat qui peut retourner du texte et une image (pas simplement juste un modèle générateur d'image) Exemples d'utilisation : Création : Via un simple prompt textuel. Modification : En passant l'image existante (tableau de bytes) et les instructions de modification (prompt). Assemblage : En passant plusieurs images (en bytes) et les instructions d'intégration (prompt). Message clé : Toutes ces fonctionnalités sont accessibles en Java, sans nécessiter Python. Générer des vidéos IA avec le modèle Veo 3, mais en Java ! https://glaforge.dev/posts/2025/09/10/generating-videos-in-java-with-veo3/ Génération de vidéos en Java avec Veo 3 (via le GenAI Java SDK de Google). Veo 3: Annoncé comme GA, prix réduits, support du format 9:16, résolution jusqu'à 1080p. Création de vidéos : À partir d'une invite textuelle (prompt). À partir d'une image existante. Deux versions différentes du modèle : veo-3.0-generate-001 (qualité supérieure, plus coûteux, plus lent). veo-3.0-fast-generate-001 (qualité inférieure, moins coûteux, mais plus rapide). Rod Johnson sur ecrire des aplication agentic en Java plus facilement qu'en python avec Embabel https://medium.com/@springrod/you-can-build-better-ai-agents-in-java-than-python-868eaf008493 Rod the papa de Spring réécrit un exemple CrewAI (Python) qui génère un livre en utilisant Embabel (Java) pour démontrer la supériorité de Java L'application utilise plusieurs agents AI spécialisés : un chercheur, un planificateur de livre et des rédacteurs de chapitres Le processus suit trois étapes : recherche du sujet, création du plan, rédaction parallèle des chapitres puis assemblage CrewAI souffre de plusieurs problèmes : configuration lourde, manque de type safety, utilisation de clés magiques dans les prompts La version Embabel nécessite moins de code Java que l'original Python et moins de fichiers de configuration YAML Embabel apporte la type safety complète, éliminant les erreurs de frappe dans les prompts et améliorant l'outillage IDE La gestion de la concurrence est mieux contrôlée en Java pour éviter les limites de débit des APIs LLM L'intégration avec Spring permet une configuration externe simple des modèles LLM et hyperparamètres Le planificateur Embabel détermine automatiquement l'ordre d'exécution des actions basé sur leurs types requis L'argument principal : l'écosystème JVM offre un meilleur modèle de programmation et accès à la logique métier existante que Python Il y a pas mal de nouveaux framework agentic en Java, notamment le dernier LAngchain4j Agentic Spring lance un serie de blog posts sur les nouveautés de Spring Boot 4 https://spring.io/blog/2025/09/02/road_to_ga_introduction baseline JDK 17 mais rebase sur Jakarta 11 Kotlin 2, Jackson 3 et JUnit 6 Fonctionnalités de résilience principales de Spring : @ConcurrencyLimit, @Retryable, RetryTemplate Versioning d'API dans Spring Améliorations du client de service HTTP L'état des clients HTTP dans Spring Introduction du support Jackson 3 dans Spring Consommateur partagé - les queues Kafka dans Spring Kafka Modularisation de Spring Boot Autorisation progressive dans Spring Security Spring gRPC - un nouveau module Spring Boot Applications null-safe avec Spring Boot 4 OpenTelemetry avec Spring Boot Repos Ahead of Time (Partie 2) Web Faire de la recherche sémantique directement dans le navigateur en local, avec EmbeddingGemma et Transformers.js https://glaforge.dev/posts/2025/09/08/in-browser-semantic-search-with-embeddinggemma/ EmbeddingGemma: Nouveau modèle d'embedding (308M paramètres) de Google DeepMind. Objectif: Permettre la recherche sémantique directement dans le navigateur. Avantages clés de l'IA côté client: Confidentialité: Aucune donnée envoyée à un serveur. Coûts réduits: Pas besoin de serveurs coûteux (GPU), hébergement statique. Faible latence: Traitement instantané sans allers-retours réseau. Fonctionnement hors ligne: Possible après le chargement initial du modèle. Technologie principale: Modèle: EmbeddingGemma (petit, performant, multilingue, support MRL pour réduire la taille des vecteurs). Moteur d'inférence: Transformers.js de HuggingFace (exécute les modèles AI en JavaScript dans le navigateur). Déploiement: Site statique avec Vite/React/Tailwind CSS, déployé sur Firebase Hosting via GitHub Actions. Gestion du modèle: Fichiers du modèle trop lourds pour Git; téléchargés depuis HuggingFace Hub pendant le CI/CD. Fonctionnement de l'app: Charge le modèle, génère des embeddings pour requêtes/documents, calcule la similarité sémantique. Conclusion: Démonstration d'une recherche sémantique privée, économique et sans serveur, soulignant le potentiel de l'IA embarquée dans le navigateur. Data et Intelligence Artificielle Docker lance Cagent, une sorte de framework multi-agent IA utilisant des LLMs externes, des modèles de Docker Model Runner, avec le Docker MCP Tookit. Il propose un format YAML pour décrire les agents d'un système multi-agents. https://github.com/docker/cagent des agents “prompt driven” (pas de code) et une structure pour decrire comment ils sont deployés pas clair comment ils sont appelés a part dans la ligne de commande de cagent fait par david gageot L'owasp décrit l'independance excessive des LLM comme une vulnerabilité https://genai.owasp.org/llmrisk2023-24/llm08-excessive-agency/ L'agence excessive désigne la vulnérabilité qui permet aux systèmes LLM d'effectuer des actions dommageables via des sorties inattendues ou ambiguës. Elle résulte de trois causes principales : fonctionnalités excessives, permissions excessives ou autonomie excessive des agents LLM. Les fonctionnalités excessives incluent l'accès à des plugins qui offrent plus de capacités que nécessaire, comme un plugin de lecture qui peut aussi modifier ou supprimer. Les permissions excessives se manifestent quand un plugin accède aux systèmes avec des droits trop élevés, par exemple un accès en lecture qui inclut aussi l'écriture. L'autonomie excessive survient quand le système effectue des actions critiques sans validation humaine préalable. Un scénario d'attaque typique : un assistant personnel avec accès email peut être manipulé par injection de prompt pour envoyer du spam via la boîte de l'utilisateur. La prévention implique de limiter strictement les plugins aux fonctions minimales nécessaires pour l'opération prévue. Il faut éviter les fonctions ouvertes comme “exécuter une commande shell” au profit d'outils plus granulaires et spécifiques. L'application du principe de moindre privilège est cruciale : chaque plugin doit avoir uniquement les permissions minimales requises. Le contrôle humain dans la boucle reste essentiel pour valider les actions à fort impact avant leur exécution. Lancement du MCP registry, une sorte de méta-annuaire officiel pour référencer les serveurs MCP https://www.marktechpost.com/2025/09/09/mcp-team-launches-the-preview-version-of-the-mcp-registry-a-federated-discovery-layer-for-enterprise-ai/ MCP Registry : Couche de découverte fédérée pour l'IA d'entreprise. Fonctionne comme le DNS pour le contexte de l'IA, permettant la découverte de serveurs MCP publics ou privés. Modèle fédéré : Évite les risques de sécurité et de conformité d'un registre monolithique. Permet des sous-registres privés tout en conservant une source de vérité “upstream”. Avantages entreprises : Découverte interne sécurisée. Gouvernance centralisée des serveurs externes. Réduction de la prolifération des contextes. Support pour les agents IA hybrides (données privées/publiques). Projet open source, actuellement en version preview. Blog post officiel : https://blog.modelcontextprotocol.io/posts/2025-09-08-mcp-registry-preview/ Exploration des internals du transaction log SQL Server https://debezium.io/blog/2025/09/08/sqlserver-tx-log/ C'est un article pour les rugeux qui veulent savoir comment SQLServer marche à l'interieur Debezium utilise actuellement les change tables de SQL Server CDC en polling périodique L'article explore la possibilité de parser directement le transaction log pour améliorer les performances Le transaction log est divisé en Virtual Log Files (VLFs) utilisés de manière circulaire Chaque VLF contient des blocs (512B à 60KB) qui contiennent les records de transactions Chaque record a un Log Sequence Number (LSN) unique pour l'identifier précisément Les données sont stockées dans des pages de 8KB avec header de 96 bytes et offset array Les tables sont organisées en partitions et allocation units pour gérer l'espace disque L'utilitaire DBCC permet d'explorer la structure interne des pages et leur contenu Cette compréhension pose les bases pour parser programmatiquement le transaction log dans un prochain article Outillage Les personalités des codeurs des différents LLMs https://www.sonarsource.com/blog/the-coding-personalities-of-leading-llms-gpt-5-update/ GPT-5 minimal ne détrône pas Claude Sonnet 4 comme leader en performance fonctionnelle malgré ses 75% de réussite GPT-5 génère un code extrêmement verbeux avec 490 000 lignes contre 370 000 pour Claude Sonnet 4 sur les mêmes tâches La complexité cyclomatique et cognitive du code GPT-5 est dramatiquement plus élevée que tous les autres modèles GPT-5 introduit 3,90 problèmes par tâche réussie contre seulement 2,11 pour Claude Sonnet 4 Point fort de GPT-5 : sécurité exceptionnelle avec seulement 0,12 vulnérabilité par 1000 lignes de code Faiblesse majeure : densité très élevée de “code smells” (25,28 par 1000 lignes) nuisant à la maintenabilité GPT-5 produit 12% de problèmes liés à la complexité cognitive, le taux le plus élevé de tous les modèles Tendance aux erreurs logiques fondamentales avec 24% de bugs de type “Control-flow mistake” Réapparition de vulnérabilités classiques comme les failles d'injection et de traversée de chemin Nécessité d'une gouvernance renforcée avec analyse statique obligatoire pour gérer la complexité du code généré Pourquoi j'ai abandonné Docker pour Podman https://codesmash.dev/why-i-ditched-docker-for-podman-and-you-should-too Problème Docker : Le daemon dockerd persistant s'exécute avec des privilèges root, posant des risques de sécurité (nombreuses CVEs citées) et consommant des ressources inutilement. Solution Podman : Sans Daemon : Pas de processus d'arrière-plan persistant. Les conteneurs s'exécutent comme des processus enfants de la commande Podman, sous les privilèges de l'utilisateur. Sécurité Renforcée : Réduction de la surface d'attaque. Une évasion de conteneur compromet un utilisateur non privilégié sur l'hôte, pas le système entier. Mode rootless. Fiabilité Accrue : Pas de point de défaillance unique ; le crash d'un conteneur n'affecte pas les autres. Moins de Ressources : Pas de daemon constamment actif, donc moins de mémoire et de CPU. Fonctionnalités Clés de Podman : Intégration Systemd : Génération automatique de fichiers d'unité systemd pour gérer les conteneurs comme des services Linux standards. Alignement Kubernetes : Support natif des pods et capacité à générer des fichiers Kubernetes YAML directement (podman generate kube), facilitant le développement local pour K8s. Philosophie Unix : Se concentre sur l'exécution des conteneurs, délègue les tâches spécialisées à des outils dédiés (ex: Buildah pour la construction d'images, Skopeo pour leur gestion). Migration Facile : CLI compatible Docker : podman utilise les mêmes commandes que docker (alias docker=podman fonctionne). Les Dockerfiles existants sont directement utilisables. Améliorations incluses : Sécurité par défaut (ports privilégiés en mode rootless), meilleure gestion des permissions de volume, API Docker compatible optionnelle. Option de convertir Docker Compose en Kubernetes YAML. Bénéfices en Production : Sécurité améliorée, utilisation plus propre des ressources. Podman représente une évolution plus sécurisée et mieux alignée avec les pratiques modernes de gestion Linux et de déploiement de conteneurs. Guide Pratique (Exemple FastAPI) : Le Dockerfile ne change pas. podman build et podman run remplacent directement les commandes Docker. Déploiement en production via Systemd. Gestion d'applications multi-services avec les “pods” Podman. Compatibilité Docker Compose via podman-compose ou kompose. Détection améliorée des APIs vulnérables dans les IDEs JetBrains et Qodana - https://blog.jetbrains.com/idea/2025/09/enhanced-vulnerable-api-detection-in-jetbrains-ides-and-qodana/ JetBrains s'associe avec Mend.io pour renforcer la sécurité du code dans leurs outils Le plugin Package Checker bénéficie de nouvelles données enrichies sur les APIs vulnérables Analyse des graphes d'appels pour couvrir plus de méthodes publiques des bibliothèques open-source Support de Java, Kotlin, C#, JavaScript, TypeScript et Python pour la détection de vulnérabilités Activation des inspections via Paramètres > Editor > Inspections en recherchant “Vulnerable API” Surlignage automatique des méthodes vulnérables avec détails des failles au survol Action contextuelle pour naviguer directement vers la déclaration de dépendance problématique Mise à jour automatique vers une version non affectée via Alt+Enter sur la dépendance Fenêtre dédiée “Vulnerable Dependencies” pour voir l'état global des vulnérabilités du projet Méthodologies Le retour de du sondage de Stack Overflow sur l'usage de l'IA dans le code https://medium.com/@amareshadak/stack-overflow-just-exposed-the-ugly-truth-about-ai-coding-tools-b4f7b5992191 84% des développeurs utilisent l'IA quotidiennement, mais 46% ne font pas confiance aux résultats. Seulement 3,1% font “hautement confiance” au code généré. 66% sont frustrés par les solutions IA “presque correctes”. 45% disent que déboguer le code IA prend plus de temps que l'écrire soi-même. Les développeurs seniors (10+ ans) font moins confiance à l'IA (2,6%) que les débutants (6,1%), créant un écart de connaissances dangereux. Les pays occidentaux montrent moins de confiance - Allemagne (22%), UK (23%), USA (28%) - que l'Inde (56%). Les créateurs d'outils IA leur font moins confiance. 77% des développeurs professionnels rejettent la programmation en langage naturel, seuls 12% l'utilisent réellement. Quand l'IA échoue, 75% se tournent vers les humains. 35% des visites Stack Overflow concernent maintenant des problèmes liés à l'IA. 69% rapportent des gains de productivité personnels, mais seulement 17% voient une amélioration de la collaboration d'équipe. Coûts cachés : temps de vérification, explication du code IA aux équipes, refactorisation et charge cognitive constante. Les plateformes humaines dominent encore : Stack Overflow (84%), GitHub (67%), YouTube (61%) pour résoudre les problèmes IA. L'avenir suggère un “développement augmenté” où l'IA devient un outil parmi d'autres, nécessitant transparence et gestion de l'incertitude. Mentorat open source et défis communautaires par les gens de Microcks https://microcks.io/blog/beyond-code-open-source-mentorship/ Microcks souffre du syndrome des “utilisateurs silencieux” qui bénéficient du projet sans contribuer Malgré des milliers de téléchargements et une adoption croissante, l'engagement communautaire reste faible Ce manque d'interaction crée des défis de durabilité et limite l'innovation du projet Les mainteneurs développent dans le vide sans feedback des vrais utilisateurs Contribuer ne nécessite pas de coder : documentation, partage d'expérience, signalement de bugs suffisent Parler du project qu'on aime autour de soi est aussi super utile Microcks a aussi des questions specifiques qu'ils ont posé dans le blog, donc si vous l'utilisez, aller voir Le succès de l'open source dépend de la transformation des utilisateurs en véritables partenaires communautaires c'est un point assez commun je trouve, le ratio parlant / silencieux est tres petit et cela encourage les quelques grandes gueules La modernisation du systemes legacy, c'est pas que de la tech https://blog.scottlogic.com/2025/08/27/holistic-approach-successful-legacy-modernisation.html Un artcile qui prend du recul sur la modernisation de systemes legacy Les projets de modernisation legacy nécessitent une vision holistique au-delà du simple focus technologique Les drivers business diffèrent des projets greenfield : réduction des coûts et mitigation des risques plutôt que génération de revenus L'état actuel est plus complexe à cartographier avec de nombreuses dépendances et risques de rupture Collaboration essentielle entre Architectes, Analystes Business et Designers UX dès la phase de découverte Approche tridimensionnelle obligatoire : Personnes, Processus et Technologie (comme un jeu d'échecs 3D) Le leadership doit créer l'espace nécessaire pour la découverte et la planification plutôt que presser l'équipe Communication en termes business plutôt que techniques vers tous les niveaux de l'organisation Planification préalable essentielle contrairement aux idées reçues sur l'agilité Séquencement optimal souvent non-évident et nécessitant une analyse approfondie des interdépendances Phases projet alignées sur les résultats business permettent l'agilité au sein de chaque phase Sécurité Cyber Attaque su Musée Histoire Naturelle https://www.franceinfo.fr/internet/securite-sur-internet/cyberattaques/le-museum-nati[…]e-d-une-cyberattaque-severe-une-plainte-deposee_7430356.html Compromission massive de packages npm populaires par un malware crypto https://www.aikido.dev/blog/npm-debug-and-chalk-packages-compromised 18 packages npm très populaires compromis le 8 septembre 2025, incluant chalk, debug, ansi-styles avec plus de 2 milliards de téléchargements hebdomadaires combinés duckdb s'est rajouté à la liste Code malveillant injecté qui intercepte silencieusement l'activité crypto et web3 dans les navigateurs des utilisateurs Le malware manipule les interactions de wallet et redirige les paiements vers des comptes contrôlés par l'attaquant sans signes évidents Injection dans les fonctions critiques comme fetch, XMLHttpRequest et APIs de wallets (window.ethereum, Solana) pour intercepter le trafic Détection et remplacement automatique des adresses crypto sur multiple blockchains (Ethereum, Bitcoin, Solana, Tron, Litecoin, Bitcoin Cash) Les transactions sont modifiées en arrière-plan même si l'interface utilisateur semble correcte et légitime Utilise des adresses “sosies” via correspondance de chaînes pour rendre les échanges moins évidents à détecter Le mainteneur compromis par email de phishing provenant du faux domaine “mailto:support@npmjs.help|support@npmjs.help” enregistré 3 jours avant l'attaque sur une demande de mise a jour de son autheotnfication a deux facteurs après un an Aikido a alerté le mainteneur via Bluesky qui a confirmé la compromission et commencé le nettoyage des packages Attaque sophistiquée opérant à plusieurs niveaux: contenu web, appels API et manipulation des signatures de transactions Les anti-cheats de jeux vidéo : une faille de sécurité majeure ? - https://tferdinand.net/jeux-video-et-si-votre-anti-cheat-etait-la-plus-grosse-faille/ Les anti-cheats modernes s'installent au Ring 0 (noyau système) avec privilèges maximaux Ils obtiennent le même niveau d'accès que les antivirus professionnels mais sans audit ni certification Certains exploitent Secure Boot pour se charger avant le système d'exploitation Risque de supply chain : le groupe APT41 a déjà compromis des jeux comme League of Legends Un attaquant infiltré pourrait désactiver les solutions de sécurité et rester invisible Menace de stabilité : une erreur peut empêcher le démarrage du système (référence CrowdStrike) Conflits possibles entre différents anti-cheats qui se bloquent mutuellement Surveillance en temps réel des données d'utilisation sous prétexte anti-triche Dérive dangereuse selon l'auteur : des entreprises de jeux accèdent au niveau EDR Alternatives limitées : cloud gaming ou sandboxing avec impact sur performances donc faites gaffe aux jeux que vos gamins installent ! Loi, société et organisation Luc Julia au Sénat - Monsieur Phi réagi et publie la vidéo Luc Julia au Sénat : autopsie d'un grand N'IMPORTE QUOI https://www.youtube.com/watch?v=e5kDHL-nnh4 En format podcast de 20 minutes, sorti au même moment et à propos de sa conf à Devoxx https://www.youtube.com/watch?v=Q0gvaIZz1dM Le lab IA - Jérôme Fortias - Et si Luc Julia avait raison https://www.youtube.com/watch?v=KScI5PkCIaE Luc Julia au Senat https://www.youtube.com/watch?v=UjBZaKcTeIY Luc Julia se défend https://www.youtube.com/watch?v=DZmxa7jJ8sI Intelligence artificielle : catastrophe imminente ? - Luc Julia vs Maxime Fournes https://www.youtube.com/watch?v=sCNqGt7yIjo Tech and Co Monsieur Phi vs Luc Julia (put a click) https://www.youtube.com/watch?v=xKeFsOceT44 La tronche en biais https://www.youtube.com/live/zFwLAOgY0Wc Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 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) 5-6 novembre 2025 : Red Hat Summit: Connect Paris 2025 - 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) 14-17 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) 4 septembre 2026 : JUG SUmmer Camp 2026 - La Rochelle (France) 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/

This interview was recorded for GOTO Unscripted.https://gotopia.techRead the full transcription of this interview hereMichelle Frost - AI Advocate at JetBrains & Responsible AI ConsultantHannes Lowette - Principal Consultant at Axxes, Monolith Advocate, Speaker & Whiskey LoverRESOURCESMichellehttps://bsky.app/profile/aiwithmichelle.comhttps://www.linkedin.com/in/michelle-frost-devHanneshttps://bsky.app/profile/hanneslowette.nethttps://twitter.com/hannes_lowettehttps://github.com/Belenarhttps://linkedin.com/in/hanneslowetteDESCRIPTIONAI advocate Michelle Frost and principal consultant Hannes Lowette discuss ethical challenges in AI development. They explore the balance between competing values like accuracy versus fairness, recent US regulatory rollbacks under the Trump administration, and market disruptions from innovations like Deep Seek.While Michelle acknowledges concerns about bias in unregulated models, she remains optimistic about AI's potential to improve lives if developed responsibly. She emphasizes the importance of transparency, bias measurement, and focusing on beneficial applications while advocating for individual and corporate accountability in the absence of comprehensive regulation.RECOMMENDED BOOKSMark Coeckelbergh • AI EthicsDebbie Sue Jancis • AI EthicsMohammad Rubyet Islam • Generative AI, Cybersecurity, and EthicsJeet Pattanaik • Ethics in AICrossing BordersCrossing Borders is a podcast by Neema, a cross border payments platform that...Listen on: Apple Podcasts SpotifyBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Tony Cardella is a seasoned software engineer based in Houston, Texas. With a robust background in enterprise development, Tony brings deep expertise in the .NET Framework (C#), Python, and cloud platforms including Microsoft Azure and Amazon Web Services. His technical repertoire spans both relational databases — such as SQL Server, MySQL, and PostgreSQL — and NoSQL solutions like Azure Cosmos DB.   Tony is a strong advocate for developer productivity tools, frequently leveraging JetBrains products including ReSharper, DataGrip, PyCharm, and Rider, as well as Visual Studio. Outside the world of code, Tony is equally passionate about strength training, whether he's lifting weights himself or coaching others in the discipline.   Topics of Discussion: [1:34] Tony shares his career journey, starting with a consulting company that reached out to him while he was job hunting. [3:17] NCrunch is an automated testing tool that runs unit tests continuously, focusing on impacted tests. [5:08] Challenges and benefits of NCrunch, and why would we need to use it? [7:44] Tony shares his approach to unit testing, focusing on covering 80% of the code with minimal effort and addressing the remaining 20% as needed. [8:51] The importance of not over-investing in unit tests that may not provide significant value. [11:47] Tony explains how Ncrunch provides code coverage metrics and visual indicators of covered and uncovered code. [12:59] The tool's ability to show exactly where unit tests are failing, without needing to dive into stack traces. [13:51] Distributed processing and integration tests. [27:44] The challenges of running integration tests with external dependencies, such as databases. [29:18] Exploratory testing and code quality. [32:34] Tony emphasizes the value of unit tests in codifying tribal knowledge and ensuring code quality.   Mentioned in this Episode: Clear Measure Way Architect Forum Software Engineer Forum Tony Cardella Lightning Talks! The Code Gorilla Survey: Fixing Bugs Stealing Time from Development NCrunch   Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.

Hi, Spring fans! In this installment, we talk to Andrey Belyaev, a Product Manager at JetBrains working on the IntelliJ IDEA product, about the latest-and-greatest support for Spring in Jetbrains IntelliJ IDEA!

В современном IT есть два пути к бесконечным деньгам. Один из них – быть топовым AI рисерчером и ждать, пока позвонит Цукерберг. Второй – поддерживать код на COBOL, от которого зависит вся мировая банковская система. В этом классическом языковом выпуске вместе с Сергеем Куксом, principal инженером из JetBrains, разбираемся с тем, что когда-то помогло COBOL стать таким популярным, и как он продолжает развиваться по сегодняшний день. Также ждем вас, ваши лайки, репосты и комменты в мессенджерах и соцсетях!
 Telegram-чат: https://t.me/podlodka Telegram-канал: https://t.me/podlodkanews Страница в Facebook: www.facebook.com/podlodkacast/ Twitter-аккаунт: https://twitter.com/PodcastPodlodka Ведущие в выпуске: Катя Петрова, Егор Толстой Полезные ссылки: Документация COBOL для новичков https://www.microfocus.com/documentation/visual-cobol/vc80/EclWin/index.html?t=GUID-7D9BBE92-0AD8-4DDC-975A-FA92A55D7187.html Про Micro Focus https://www.opentext.com/about/brands/microfocus Как работает фича ReportWriter https://www.microfocus.com/documentation/visual-cobol/vc80/VS2022/index.html?t=HRLHLHWRI01.html Awesome COBOL https://github.com/loveOSS/awesome-cobol

If you've ever used IntelliJ IDEA, PyCharm, or Rider, you've probably already felt the impact of JetBrains. But what happens when one of the world's most trusted software development toolmakers starts asking whether developers should completely rethink their identity? In this episode of Tech Talks Daily, I caught up with Kirill Skrygan, the CEO of JetBrains. Kirill's story is something many developers will admire. He joined JetBrains as a junior developer back in 2010 and steadily worked his way through the ranks. Along the way, he helped launch new products, led remote development during the pandemic, and is now steering the company into the AI era. This isn't just about adding AI features to tools. Kirill is challenging long-held assumptions about what it means to be a software engineer. He believes we're entering a new chapter where non-technical creators will build their own tools, and where proactive AI agents will help maintain and even update code automatically. It's a bold vision, but one grounded in practical experience and responsibility. JetBrains is used by over 11 million developers, including 88 of the Fortune Global Top 100, so the stakes are high. We explored how AI is lowering the barrier to software creation, what that means for traditional developers, and why Kirill believes the shift won't devalue their skills but rather evolve them. He shared insights into JetBrains' own AI agent, Junie, which is already being used in production environments. He also talked about Kineta, their no-code platform designed for a new generation of creators, including those who want to build apps without a computer science background. There's also an honest discussion around the friction points of this new wave, especially when non-engineers build tools that later need to be secured and maintained. Kirill didn't shy away from the complexity of that challenge, but he's optimistic that the industry can solve it. Toward the end of the conversation, we reflected on JetBrains' role as an independent company that doesn't chase hype or rely on VC funding. That independence gives them the freedom to build what developers actually need, rather than what might look good on a press release. This episode is a thoughtful look at the future of software development, leadership from inside the codebase, and the evolving relationship between humans and AI in one of tech's most foundational professions. If you're a developer, CTO, product manager, or simply curious about how AI is changing the craft of coding, this one's worth your time.

 This week on the PHP Podcast, Eric and John discuss Spec-driven Development with Kiro, JetBrains on Huggingface, Event Sourcing with Laravel Verbs, Automating your life with n8n, PHP Tek 2026 Website development using vibe coding, and more. Links from the show: Introducing Kiro – Kiro JetBrains (JetBrains) Verbs About Grokability – Snipe-IT Free open […] The post The PHP Podcast: 2025.07.17 appeared first on PHP Architect.

Пока одни скромно просят ChatGPT написать пару скриптов, другие уже вовсю интегрируют всё многообразие AI-моделей и инструментов во все сферы своей жизни, стремясь максимально оптимизировать процессы, минимизировать рутину и расширить границы возможного. Как? Разбираемся с Денисом Ширяевым, автором канала Denis Sexy IT. Денис использует AI практически для всего: от работы и хобби до здоровья и саморазвития, и в выпуске мы обсудили его опыт применения АI, текущие ограничения, различия между моделями и лучшие практики работы с ними. Также ждем вас, ваши лайки, репосты и комменты в мессенджерах и соцсетях!
 Telegram-чат: https://t.me/podlodka Telegram-канал: https://t.me/podlodkanews Страница в Facebook: www.facebook.com/podlodkacast/ Twitter-аккаунт: https://twitter.com/PodcastPodlodka Ведущие в выпуске: Катя Петрова, Егор Толстой, Аня Симонова Полезные ссылки: Анализ SEO-контента конкурентов и создание лендингов на базе этой инфы с помощью LLM https://t.me/c/1051500113/8777 Подготовка к интервью в JetBrains https://t.me/c/1051500113/10106 Программа для восстановления шеи от o1 Pro https://t.me/c/1051500113/9436 Канал Дениса с другими постами https://t.me/s/denissexy

Varun Mohan is the co-founder and CEO of Windsurf (formerly Codeium), an AI-powered development environment (IDE) that has been used by over 1 million developers in just four months and has quickly emerged as a leader in transforming how developers build software. Prior to finding success with Windsurf, the company pivoted twice—first from GPU virtualization infrastructure to an IDE plugin, and then to their own standalone IDE.In this conversation, you'll learn:1. Why Windsurf walked away from a profitable GPU infrastructure business and bet the company on helping engineers code2. The surprising UI discovery that tripled adoption rates overnight.3. The secret behind Windsurf's B2B enterprise plan, and why they invested early in an 80-person sales team despite conventional startup wisdom.4. How non-technical staff at Windsurf built their own custom tools instead of purchasing SaaS products, saving them over $500k in software costs5. Why Varun believes 90% of code will be AI-generated, but engineering jobs will actually increase6. How training on millions of incomplete code samples gives Windsurf an edge, and creates a moat long-term7. Why agency is the most undervalued and important skill in the AI era—Brought to you by:• Brex—The banking solution for startups• Productboard—Make products that matter• Coda—The all-in-one collaborative workspace—Where to find Varun Mohan:• X: https://x.com/_mohansolo• LinkedIn: https://www.linkedin.com/in/varunkmohan/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Varun's background(03:57) Building and scaling Windsurf(12:58) Windsurf: The new purpose-built IDE to harness magic(17:11) The future of engineering and AI(21:30) Skills worth investing in(23:07) Hiring philosophy and company culture(35:22) Sales strategy and market position(39:37) JetBrains vs. VS Code: extensibility and enterprise adoption(41:20) Live demo: building an Airbnb for dogs with Windsurf(42:46) Tips for using Windsurf effectively(46:38) AI's role in code modification and review(48:56) Empowering non-developers to build custom software(54:03) Training Windsurf(01:00:43) Windsurf's unique team structure and product strategy(01:06:40) The importance of continuous innovation(01:08:57) Final thoughts and advice for aspiring developers—Referenced:• Windsurf: https://windsurf.com/• VS Code: https://code.visualstudio.com/• JetBrains: https://www.jetbrains.com/• Eclipse: https://eclipseide.org/• Visual Studio: https://visualstudio.microsoft.com/• Vim: https://www.vim.org/• Emacs: https://www.gnu.org/software/emacs/• Lessons from a two-time unicorn builder, 50-time startup advisor, and 20-time company board member | Uri Levine (co-founder of Waze): https://www.lennysnewsletter.com/p/lessons-from-uri-levine• IntelliJ: https://www.jetbrains.com/idea/• Julia: https://julialang.org/• Parallel computing: https://en.wikipedia.org/wiki/Parallel_computing• Douglas Chen on LinkedIn: https://www.linkedin.com/in/douglaspchen/• Carlos Delatorre on LinkedIn: https://www.linkedin.com/in/cadelatorre/• MongoDB: https://www.mongodb.com/• Cursor: https://www.cursor.com/• GitHub Copilot: https://github.com/features/copilot• Llama: https://www.llama.com/• Mistral: https://mistral.ai/• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (CEO and co-founder): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder & CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons• Behind the product: Replit | Amjad Masad (co-founder and CEO): https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad• React: https://react.dev/• Sonnet: https://www.anthropic.com/claude/sonnet• OpenAI: https://openai.com/• FedRamp: https://www.fedramp.gov/• Dario Amodei on LinkedIn: https://www.linkedin.com/in/dario-amodei-3934934/• Amdahl's law: https://en.wikipedia.org/wiki/Amdahl%27s_law• How to win in the AI era: Ship a feature every week, embrace technical debt, ruthlessly cut scope, and create magic your competitors can't copy | Gaurav Misra (CEO and co-founder of Captions): https://www.lennysnewsletter.com/p/how-to-win-in-the-ai-era-gaurav-misra—Recommended book:• Fall in Love with the Problem, Not the Solution: A Handbook for Entrepreneurs: https://www.amazon.com/Fall-Love-Problem-Solution-Entrepreneurs/dp/1637741987—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

We are happy to announce that there will be a dedicated MCP track at the 2025 AI Engineer World's Fair, taking place Jun 3rd to 5th in San Francisco, where the MCP core team and major contributors and builders will be meeting. Join us and apply to speak or sponsor!When we first wrote Why MCP Won, we had no idea how quickly it was about to win.In the past 4 weeks, OpenAI and now Google have now announced the MCP support, effectively confirming our prediction that MCP was the presumptive winner of the agent standard wars. MCP has now overtaken OpenAPI, the incumbent option and most direct alternative, in GitHub stars (3 months ahead of conservative trendline):We have explored the state of MCP at AIE (now the first ever >100k views workshop):And since then, we've added a 7th reason why MCP won - this team acts very quickly on feedback, with the 2025-03-26 spec update adding support for stateless/resumable/streamable HTTP transports, and comprehensive authz capabilities based on OAuth 2.1.This bodes very well for the future of the community and project. For protocol and history nerds, we also asked David and Justin to tell the origin story of MCP, which we leave to the reader to enjoy (you can also skim the transcripts, or, the changelogs of a certain favored IDE). It's incredible the impact that individual engineers solving their own problems can have on an entire industry.Full video episodeLike and subscribe on YouTube!Show Links* David* Justin* MCP* Why MCP WonTimestamps* 00:00 Introduction and Guest Welcome* 00:37 What is MCP?* 02:00 The Origin Story of MCP* 05:18 Development Challenges and Solutions* 08:06 Technical Details and Inspirations* 29:45 MCP vs Open API* 32:48 Building MCP Servers* 40:39 Exploring Model Independence in LLMs* 41:36 Building Richer Systems with MCP* 43:13 Understanding Agents in MCP* 45:45 Nesting and Tool Confusion in MCP* 49:11 Client Control and Tool Invocation* 52:08 Authorization and Trust in MCP Servers* 01:01:34 Future Roadmap and Stateless Servers* 01:10:07 Open Source Governance and Community Involvement* 01:18:12 Wishlist and Closing RemarksTranscriptAlessio [00:00:02]: Hey, everyone. Welcome back to Latent Space. This is Alessio, partner and CTO at Decibel, and I'm joined by my co-host Swyx, founder of Small AI.swyx [00:00:10]: Hey, morning. And today we have a remote recording, I guess, with David and Justin from Anthropic over in London. Welcome. Hey, good You guys have created a storm of hype because of MCP, and I'm really glad to have you on. Thanks for making the time. What is MCP? Let's start with a crisp what definition from the horse's mouth, and then we'll go into the origin story. But let's start off right off the bat. What is MCP?Justin/David [00:00:43]: Yeah, sure. So Model Context Protocol, or MCP for short, is basically something we've designed to help AI applications extend themselves or integrate with an ecosystem of plugins, basically. The terminology is a bit different. We use this client-server terminology, and we can talk about why that is and where that came from. But at the end of the day, it really is that. It's like extending and enhancing the functionality of AI application.swyx [00:01:05]: David, would you add anything?Justin/David [00:01:07]: Yeah, I think that's actually a good description. I think there's like a lot of different ways for how people are trying to explain it. But at the core, I think what Justin said is like extending AI applications is really what this is about. And I think the interesting bit here that I want to highlight, it's AI applications and not models themselves that this is focused on. That's a common misconception that we can talk about a bit later. But yeah. Another version that we've used and gotten to like is like MCP is kind of like the USB-C port of AI applications and that it's meant to be this universal connector to a whole ecosystem of things.swyx [00:01:44]: Yeah. Specifically, an interesting feature is, like you said, the client and server. And it's a sort of two-way, right? Like in the same way that said a USB-C is two-way, which could be super interesting. Yeah, let's go into a little bit of the origin story. There's many people who've tried to make statistics. There's many people who've tried to build open source. I think there's an overall, also, my sense is that Anthropic is going hard after developers in the way that other labs are not. And so I'm also curious if there was any external influence or was it just you two guys just in a room somewhere riffing?Justin/David [00:02:18]: It is actually mostly like us two guys in a room riffing. So this is not part of a big strategy. You know, if you roll back time a little bit and go into like July 2024. I was like, started. I started at Anthropic like three months earlier or two months earlier. And I was mostly working on internal developer tooling, which is what I've been doing for like years and years before. And as part of that, I think there was an effort of like, how do I empower more like employees at Anthropic to use, you know, to integrate really deeply with the models we have? Because we've seen these, like, how good it is, how amazing it will become even in the future. And of course, you know, just dogfoot your own model as much as you can. And as part of that. From my development tooling background, I quickly got frustrated by the idea that, you know, on one hand side, I have Cloud Desktop, which is this amazing tool with artifacts, which I really enjoyed. But it was very limited to exactly that feature set. And it was there was no way to extend it. And on the other hand side, I like work in IDEs, which could greatly like act on like the file system and a bunch of other things. But then they don't have artifacts or something like that. And so what I constantly did was just copy. Things back and forth on between Cloud Desktop and the IDE, and that quickly got me, honestly, just very frustrated. And part of that frustration wasn't like, how do I go and fix this? What, what do we need? And back to like this development developer, like focus that I have, I really thought about like, well, I know how to build all these integrations, but what do I need to do to let these applications let me do this? And so it's very quickly that you see that this is clearly like an M times N problem. Like you have multiple like applications. And multiple integrations you want to build and like, what that is better there to fix this than using a protocol. And at the same time, I was actually working on an LSP related thing internally that didn't go anywhere. But you put these things together in someone's brain and let them wait for like a few weeks. And out of that comes like the idea of like, let's build some, some protocol. And so back to like this little room, like it was literally just me going to a room with Justin and go like, I think we should build something like this. Uh, this is a good idea. And Justin. Lucky for me, just really took an interest in the idea, um, and, and took it from there to like, to, to build something, together with me, that's really the inception story is like, it's us to, from then on, just going and building it over, over the course of like, like a month and a half of like building the protocol, building the first integration, like Justin did a lot of the, like the heavy lifting of the first integrations in cloud desktop. I did a lot of the first, um, proof of concept of how this can look like in an IDE. And if you, we could talk about like some of. All the tidbits you can find way before the inception of like before the official release, if you were looking at the right repositories at the right time, but there you go. That's like some of the, the rough story.Alessio [00:05:12]: Uh, what was the timeline when, I know November 25th was like the official announcement date. When did you guys start working on it?Justin/David [00:05:19]: Justin, when did we start working on that? I think it, I think it was around July. I think, yeah, I, as soon as David pitched this initial idea, I got excited pretty quickly and we started working on it, I think. I think almost immediately after that conversation and then, I don't know, it was a couple, maybe a few months of, uh, building the really unrewarding bits, if we're being honest, because for, for establishing something that's like this communication protocol has clients and servers and like SDKs everywhere, there's just like a lot of like laying the groundwork that you have to do. So it was a pretty, uh, that was a pretty slow couple of months. But then afterward, once you get some things talking over that wire, it really starts to get exciting and you can start building. All sorts of crazy things. And I think this really came to a head. And I don't remember exactly when it was, maybe like approximately a month before release, there was an internal hackathon where some folks really got excited about MCP and started building all sorts of crazy applications. I think the coolest one of which was like an MCP server that can control a 3d printer or something. And so like, suddenly people are feeling this power of like cloud connecting to the outside world in a really tangible way. And that, that really added some, uh, some juice to us and to the release.Alessio [00:06:32]: Yeah. And we'll go into the technical details, but I just want to wrap up here. You mentioned you could have seen some things coming if you were looking in the right places. We always want to know what are the places to get alpha, how, how, how to find MCP early.Justin/David [00:06:44]: I'm a big Zed user. I liked the Zed editor. The first MCP implementation on an IDE was in Zed. It was written by me and it was there like a month and a half before the official release. Just because we needed to do it in the open because it's an open source project. Um, and so it was, it was not, it was named slightly differently because we. We were not set on the name yet, but it was there.swyx [00:07:05]: I'm happy to go a little bit. Anthropic also had some preview of a model with Zed, right? Some kind of fast editing, uh, model. Um, uh, I, I'm con I confess, you know, I'm a cursor windsurf user. Haven't tried Zed. Uh, what's, what's your, you know, unrelated or, you know, unsolicited two second pitch for, for Zed. That's a good question.Justin/David [00:07:28]: I, it really depends what you value in editors. For me. I, I wouldn't even say I like, I love Zed more than others. I like them all like complimentary in, in a way or another, like I do use windsurf. I do use Zed. Um, but I think my, my main pitch for Zed is low latency, super smooth experience editor with a decent enough AI integration.swyx [00:07:51]: I mean, and maybe, you know, I think that's, that's all it is for a lot of people. Uh, I think a lot of people obviously very tied to the VS code paradigm and the extensions that come along with it. Okay. So I wanted to go back a little bit. You know, on, on, on some of the things that you mentioned, Justin, uh, which was building MCP on paper, you know, obviously we only see the end result. It just seems inspired by LSP. And I, I think both of you have acknowledged that. So how much is there to build? And when you say build, is it a lot of code or a lot of design? Cause I felt like it's a lot of design, right? Like you're picking JSON RPC, like how much did you base off of LSP and, and, you know, what, what, what was the sort of hard, hard parts?Justin/David [00:08:29]: Yeah, absolutely. I mean, uh, we, we definitely did take heavy inspiration from LSP. David had much more prior experience with it than I did working on developer tools. So, you know, I've mostly worked on products or, or sort of infrastructural things. LSP was new to me. But as a, as a, like, or from design principles, it really makes a ton of sense because it does solve this M times N problem that David referred to where, you know, in the world before LSP, you had all these different IDEs and editors, and then all these different languages that each wants to support or that their users want them to support. And then everyone's just building like one. And so, like, you use Vim and you might have really great support for, like, honestly, I don't know, C or something, and then, like, you switch over to JetBrains and you have the Java support, but then, like, you don't get to use the great JetBrains Java support in Vim and you don't get to use the great C support in JetBrains or something like that. So LSP largely, I think, solved this problem by creating this common language that they could all speak and that, you know, you can have some people focus on really robust language server implementations, and then the IDE developers can really focus on that side. And they both benefit. So that was, like, our key takeaway for MCP is, like, that same principle and that same problem in the space of AI applications and extensions to AI applications. But in terms of, like, concrete particulars, I mean, we did take JSON RPC and we took this idea of bidirectionality, but I think we quickly took it down a different route after that. I guess there is one other principle from LSP that we try to stick to today, which is, like, this focus on how features manifest. More than. The semantics of things, if that makes sense. David refers to it as being presentation focused, where, like, basically thinking and, like, offering different primitives, not because necessarily the semantics of them are very different, but because you want them to show up in the application differently. Like, that was a key sort of insight about how LSP was developed. And that's also something we try to apply to MCP. But like I said, then from there, like, yeah, we spent a lot of time, really a lot of time, and we could go into this more separately, like, thinking about each of the primitives that we want to offer in MCP. And why they should be different, like, why we want to have all these different concepts. That was a significant amount of work. That was the design work, as you allude to. But then also already out of the gate, we had three different languages that we wanted to at least support to some degree. That was TypeScript, Python, and then for the Z integration, it was Rust. So there was some SDK building work in those languages, a mixture of clients and servers to build out to try to create this, like, internal ecosystem that we could start playing with. And then, yeah, I guess just trying to make everything, like, robust over, like, I don't know, this whole, like, concept that we have for local MCP, where you, like, launch subprocesses and stuff and making that robust took some time as well. Yeah, maybe adding to that, I think the LSP inference goes even a little bit further. Like, we did take actually quite a look at criticisms on LSP, like, things that LSP didn't do right and things that people felt they would love to have different and really took that to heart to, like, see, you know, what are some of the things. that we wish, you know, we should do better. We took a, you know, like, a lengthy, like, look at, like, their very unique approach to JSON RPC, I may say, and then we decided that this is not what we do. And so there's, like, these differences, but it's clearly very, very inspired. Because I think when you're trying to build and focus, if you're trying to build something like MCP, you kind of want to pick the areas you want to innovate in, but you kind of want to be boring about the other parts in pattern matching LSP. So the problem allows you to be boring in a lot of the core pieces that you want to be boring in. Like, the choice of JSON RPC is very non-controversial to us because it's just, like, it doesn't matter at all, like, what the action, like, bites on the bar that you're speaking. It makes no difference to us. The innovation is on the primitives you choose and these type of things. And so there's way more focus on that that we wanted to do. So having some prior art is good there, basically.swyx [00:12:26]: It does. I wanted to double click. I mean, there's so many things you can go into. Obviously, I am passionate about protocol design. I wanted to show you guys this. I mean, I think you guys know, but, you know, you already referred to the M times N problem. And I can just share my screen here about anyone working in developer tools has faced this exact issue where you see the God box, basically. Like, the fundamental problem and solution of all infrastructure engineering is you have things going to N things, and then you put the God box and they'll all be better, right? So here is one problem for Uber. One problem for... GraphQL, one problem for Temporal, where I used to work at, and this is from React. And I was just kind of curious, like, you know, did you solve N times N problems at Facebook? Like, it sounds like, David, you did that for a living, right? Like, this is just N times N for a living.Justin/David [00:13:16]: David Pérez- Yeah, yeah. To some degree, for sure. I did. God, what a good example of this, but like, I did a bunch of this kind of work on like source control systems and these type of things. And so there were a bunch of these type of problems. And so you just shove them into something that everyone can read from and everyone can write to, and you build your God box somewhere, and it works. But yeah, it's just in developer tooling, you're absolutely right. In developer tooling, this is everywhere, right?swyx [00:13:47]: And that, you know, it shows up everywhere. And what was interesting is I think everyone who makes the God box then has the same set of problems, which is also you now have like composability off and remotes versus local. So, you know, there's this very common set of problems. So I kind of want to take a meta lesson on how to do the God box, but, you know, we can talk about the sort of development stuff later. I wanted to double click on, again, the presentation that Justin mentioned of like how features manifest and how you said some things are the same, but you just want to reify some concepts so they show up differently. And I had that sense, you know, when I was looking at the MCP docs, I'm like, why do these two things need to be the difference in other? I think a lot of people treat tool calling as the solution to everything, right? And sometimes you can actually sort of view kinds of different kinds of tool calls as different things. And sometimes they're resources. Sometimes they're actually taking actions. Sometimes they're something else that I don't really know yet. But I just want to see, like, what are some things that you sort of mentally group as adjacent concepts and why were they important to you to emphasize?Justin/David [00:14:58]: Yeah, I can chat about this a bit. I think fundamentally we every sort of primitive that we thought through, we thought from the perspective of the application developer first, like if I'm building an application, whether it is an IDE or, you know, call a desktop or some agent interface or whatever the case may be, what are the different things that I would want to receive from like an integration? And I think once you take that lens, it becomes quite clear that that tool calling is necessary, but very insufficient. Like there are many other things you would want to do besides just get tools. And plug them into the model and you want to have some way of differentiating what those different things are. So the kind of core primitives that we started MCP with, we've since added a couple more, but the core ones are really tools, which we've already talked about. It's like adding, adding tools directly to the model or function calling is sometimes called resources, which is basically like bits of data or context that you might want to add to the context. So excuse me, to the, to the model context. And this, this is the first primitive where it's like, we, we. Decided this could be like application controlled, like maybe you want a model to automatically search through and, and find relevant resources and bring them into context. But maybe you also want that to be an explicit UI affordance in the application where the user can like, you know, pick through a dropdown or like a paperclip menu or whatever, and find specific things and tag them in. And then that becomes part of like their message to the LLM. Like those are both use cases for resources. And then the third one is prompts. Which are deliberately meant to be like user initiated or. Like. User substituted. Text or messages. So like the analogy here would be like, if you're an editor, like a slash command or something like that, or like an at, you know, auto completion type thing where it's like, I have this kind of macro effectively that I want to drop in and use. And we have sort of expressed opinions through MCP about the different ways that these things could manifest, but ultimately it is for application developers to decide, okay, you, you get these different concepts expressed differently. Um, and it's very useful as an application developer because you can decide. The appropriate experience for each, and actually this can be a point of differentiation to, like, we were also thinking, you know, from the application developer perspective, they, you know, application developers don't want to be commoditized. They don't want the application to end up the same as every other AI application. So like, what are the unique things that they could do to like create the best user experience even while connecting up to this big open ecosystem of integration? I, yeah. And I think to add to that, the, I think there are two, two aspects to that, that I want to. I want to mention the first one is that interestingly enough, like while nowadays tool calling is obviously like probably like 95% plus of the integrations, and I wish there would be, you know, more clients doing tool resources, doing prompts. The, the very first implementation in that is actually a prompt implementation. It doesn't deal with tools. And, and it, we found this actually quite useful because what it allows you to do is, for example, build an MCP server that takes like a backtrack. So it's, it's not necessarily like a tool that literally just like rawizes from Sentry or any other like online platform that, that tracks your, your crashes. And just lets you pull this into the context window beforehand. And so it's quite nice that way that it's like a user driven interaction that you does the user decide when to pull this in and don't have to wait for the model to do it. And so it's a great way to craft the prompt in a way. And I think similarly, you know, I wish, you know, more MCP servers today would bring prompts as examples of, like how to even use the tools. Yeah. at the same time. The resources bits are quite interesting as well. And I wish we would see more usage there because it's very easy to envision, but yet nobody has really implemented it. A system where like an MCP server exposes, you know, a set of documents that you have, your database, whatever you might want to as a set of resources. And then like a client application would build a full rack index around this, right? This is definitely an application use case we had in mind as to why these are exposed in such a way that they're not model driven, because you might want to have way more resource content than is, you know, realistically usable in a context window. And so I think, you know, I wish applications and I hope applications will do this in the next few months, use these primitives, you know, way better, because I think there's way more rich experiences to be created that way. Yeah, completely agree with that. And I would also add that I would go into it if I haven't.Alessio [00:19:30]: I think that's a great point. And everybody just, you know, has a hammer and wants to do tool calling on everything. I think a lot of people do tool calling to do a database query. They don't use resources for it. What are like the, I guess, maybe like pros and cons or like when people should use a tool versus a resource, especially when it comes to like things that do have an API interface, like for a database, you can do a tool that does a SQL query versus when should you do that or a resource instead with the data? Yeah.Justin/David [00:20:00]: The way we separate these is like tools are always meant to be initiated by the model. It's sort of like at the model's discretion that it will like find the right tool and apply it. So if that's the interaction you want as a server developer, where it's like, okay, this, you know, suddenly I've given the LLM the ability to run a SQL queries, for example, that makes sense as a tool. But resources are more flexible, basically. And I think, to be completely honest, the story here is practically a bit complicated today. Because many clients don't support resources yet. But like, I think in an ideal world where all these concepts are fully realized, and there's like full ecosystem support, you would do resources for things like the schemas of your database tables and stuff like that, as a way to like either allow the user to say like, okay, now, you know, cloud, I want to talk to you about this database table. Here it is. Let's have this conversation. Or maybe the particular AI application that you're using, like, you know, could be something agentic, like cloud code. is able to just like agentically look up resources and find the right schema of the database table you're talking about, like both those interactions are possible. But I think like, anytime you have this sort of like, you want to list a bunch of entities, and then read any of them, that makes sense to model as resources. Resources are also, they're uniquely identified by a URI, always. And so you can also think of them as like, you know, sort of general purpose transformers, even like, if you want to support an interaction where a user just like drops a URI in, and then you like automatically figure out how to interpret that, you could use MCP servers to do that interpretation. One of the interesting side notes here, back to the Z example of resources, is that has like a prompt library that you can do, that people can interact with. And we just exposed a set of default prompts that we want everyone to have as part of that prompt library. Yeah, resources for a while so that like, you boot up Zed and Zed will just populate the prompt library from an MCP server, which was quite a cool interaction. And that was, again, a very specific, like, both sides needed to agree upon the URI format and the underlying data format. And but that was a nice and kind of like neat little application of resources. There's also going back to that perspective of like, as an application developer, what are the things that I would want? Yeah. We also applied this thinking to like, you know, like, we can do this, we can do this, we can do this, we can do this. Like what existing features of applications could conceivably be kind of like factored out into MCP servers if you were to take that approach today. And so like basically any IDE where you have like an attachment menu that I think naturally models as resources. It's just, you know, those implementations already existed.swyx [00:22:49]: Yeah, I think the immediate like, you know, when you introduced it for cloud desktop and I saw the at sign there, I was like, oh, yeah, that's what Cursor has. But this is for everyone else. And, you know, I think like that that is a really good design target because it's something that already exists and people can map on pretty neatly. I was actually featuring this chart from Mahesh's workshop that presumably you guys agreed on. I think this is so useful that it should be on the front page of the docs. Like probably should be. I think that's a good suggestion.Justin/David [00:23:19]: Do you want to do you want to do a PR for this? I love it.swyx [00:23:21]: Yeah, do a PR. I've done a PR for just Mahesh's workshop in general, just because I'm like, you know. I know.SPEAKER_03 [00:23:28]: I approve. Yeah.swyx [00:23:30]: Thank you. Yeah. I mean, like, but, you know, I think for me as a developer relations person, I always insist on having a map for people. Here are all the main things you have to understand. We'll spend the next two hours going through this. So some one image that kind of covers all this, I think is pretty helpful. And I like your emphasis on prompts. I would say that it's interesting that like I think, you know, in the earliest early days of like chat GPT and cloud, people. Often came up with, oh, you can't really follow my screen, can you? In the early days of chat of, of chat, GPT and all that, like a lot, a lot of people started like, you know, GitHub for prompts, like we'll do prop manager libraries and, and like those never really took off. And I think something like this is helpful and important. I would say like, I've also seen prompt file from human loop, I think, as, as other ways to standardize how people share prompts. But yeah, I agree that like, there should be. There should be more innovation here. And I think probably people want some dynamicism, which I think you, you afford, you allow for. And I like that you have multi-step that this was, this is the main thing that got me like, like these guys really get it. You know, I think you, you maybe have a published some research that says like, actually sometimes to get, to get the model working the right way, you have to do multi-step prompting or jailbreaking to, to, to behave the way that you want. And so I think prompts are not just single conversations. They're sometimes chains of conversations. Yeah.Alessio [00:25:05]: Another question that I had when I was looking at some server implementations, the server builders kind of decide what data gets eventually returned, especially for tool calls. For example, the Google maps one, right? If you just look through it, they decide what, you know, attributes kind of get returned and the user can not override that if there's a missing one. That has always been my gripe with like SDKs in general, when people build like API wrapper SDKs. And then they miss one parameter that maybe it's new and then I can not use it. How do you guys think about that? And like, yeah, how much should the user be able to intervene in that versus just letting the server designer do all the work?Justin/David [00:25:41]: I think we probably bear responsibility for the Google maps one, because I think that's one of the reference servers we've released. I mean, in general, for things like for tool results in particular, we've actually made the deliberate decision, at least thus far, for tool results to be not like sort of structured JSON data, not matching a schema, really, but as like a text or images or basically like messages that you would pass into the LLM directly. And so I guess the correlation that is, you really should just return a whole jumble of data and trust the LLM to like sort through it and see. I mean, I think we've clearly done a lot of work. But I think we really need to be able to shift and like, you know, extract the information it cares about, because that's what that's exactly what they excel at. And we really try to think about like, yeah, how to, you know, use LLMs to their full potential and not maybe over specify and then end up with something that doesn't scale as LLMs themselves get better and better. So really, yeah, I suppose what should be happening in this example server, which again, will request welcome. It would be great. It's like if all these result types were literally just passed through from the API that it's calling, and then the API would be able to pass through automatically.Alessio [00:26:50]: Thank you for joining us.Alessio [00:27:19]: It's a hard to sign decisions on where to draw the line.Justin/David [00:27:22]: I'll maybe throw AI under the bus a little bit here and just say that Claude wrote a lot of these example servers. No surprise at all. But I do think, sorry, I do think there's an interesting point in this that I do think people at the moment still to mostly still just apply their normal software engineering API approaches to this. And I think we're still need a little bit more relearning of how to build something for LLMs and trust them, particularly, you know, as they are getting significantly better year to year. Right. And I think, you know, two years ago, maybe that approach would have been very valid. But nowadays, just like just throw data at that thing that is really good at dealing with data is a good approach to this problem. And I think it's just like unlearning like 20, 30, 40 years of software engineering practices that go a little bit into this to some degree. If I could add to that real quickly, just one framing as well for MCP is thinking in terms of like how crazily fast AI is advancing. I mean, it's exciting. It's also scary. Like thinking, us thinking that like the biggest bottleneck to, you know, the next wave of capabilities for models might actually be their ability to like interact with the outside world to like, you know, read data from outside data sources or like take stateful actions. Working at Anthropic, we absolutely care about doing that. Safely and with the right control and alignment measures in place and everything. But also as AI gets better, people will want that. That'll be key to like becoming productive with AI is like being able to connect them up to all those things. So MCP is also sort of like a bet on the future and where this is all going and how important that will be.Alessio [00:29:05]: Yeah. Yeah, I would say any API attribute that says formatted underscore should kind of be gone and we should just get the raw data from all of them. Because why, you know, why are you formatting? For me, the, the model is definitely smart enough to format an address. So I think that should go to the end user.swyx [00:29:23]: Yeah. I have, I think Alessio is about to move on to like server implementation. I wanted to, I think we were talking, we're still talking about sort of MCP design and goals and intentions. And we've, I think we've indirectly identified like some problems that MCP is really trying to address. But I wanted to give you the spot to directly take on MCP versus open API, because I think obviously there's a, this is a top question. I wanted to sort of recap everything we just talked about and give people a nice little segment that, that people can say, say, like, this is a definitive answer on MCP versus open API.Justin/David [00:29:56]: Yeah, I think fundamentally, I mean, open API specifications are a very great tool. And like I've used them a lot in developing APIs and consumers of APIs. I think fundamentally, or we think that they're just like too granular for what you want to do with LLMs. Like they don't express higher level AI specific concepts like this whole mental model. Yeah. But we've talked about with the primitives of MCP and thinking from the perspective of the application developer, like you don't get any of that when you encode this information into an open API specification. So we believe that models will benefit more from like the purpose built or purpose design tools, resources, prompts, and the other primitives than just kind of like, here's our REST API, go wild. I do think there, there's another aspect. I think that I'm not an open API expert, so I might, everything might not be perfectly accurate. But I do think that we're... Like there's been, and we can talk about this a bit more later. There's a deliberate design decision to make the protocol somewhat stateful because we do really believe that AI applications and AI like interactions will become inherently more stateful and that we're the current state of like, like need for statelessness is more a temporary point in time that will, you know, to some degree that will always exist. But I think like more statefulness will become increasingly more popular, particularly when you think about additional modalities that go beyond just pure text-based, you know, interactions with models, like it might be like video, audio, whatever other modalities exist and out there already. And so I do think that like having something a bit more stateful is just inherently useful in this interaction pattern. I do think they're actually more complimentary open API and MCP than if people wanted to make it out. Like people look. For these, like, you know, A versus B and like, you know, have, have all the, all the developers of these things go in a room and fist fight it out. But that's rarely what's going on. I think it's actually, they're very complimentary and they have their little space where they're very, very strong. And I think, you know, just use the best tool for the job. And if you want to have a rich interaction between an AI application, it's probably like, it's probably MCP. That's the right choice. And if, if you want to have like an API spec somewhere that is very easy and like a model can read. And to interpret, and that's what, what worked for you, then open API is the way to go. One more thing to add here is that we've already seen people, I mean, this happened very early. People in the community built like bridges between the two as well. So like, if what you have is an open API specification and no one's, you know, building a custom MCP server for it, there are already like translators that will take that and re-expose it as MCP. And you could do the other direction too. Awesome.Alessio [00:32:43]: Yeah. I think there's the other side of MCPs that people don't talk as much. Okay. I think there's the other side of MCPs that people don't talk as much about because it doesn't go viral, which is building the servers. So I think everybody does the tweets about like connect the cloud desktop to XMCP. It's amazing. How would you guys suggest people start with building servers? I think the spec is like, so there's so many things you can do that. It's almost like, how do you draw the line between being very descriptive as a server developer versus like going back to our discussion before, like just take the data and then let them auto manipulate it later. Do you have any suggestions for people?Justin/David [00:33:16]: I. I think there, I have a few suggestions. I think that one of the best things I think about MCP and something that we got right very early is that it's just very, very easy to build like something very simple that might not be amazing, but it's pretty, it's good enough because models are very good and get this going within like half an hour, you know? And so I think that the best part is just like pick the language of, you know, of your choice that you love the most, pick the SDK for it, if there's an SDK for it, and then just go build a tool of the thing that matters to you personally. And that you want to use. You want to see the model like interact with, build the server, throw the tool in, don't even worry too much about the description just yet, like do a bit of like, write your little description as you think about it and just give it to the model and just throw it to standard IO protocol transport wise into like an application that you like and see it do things. And I think that's part of the magic that, or like, you know, empowerment and magic for developers to get so quickly to something that the model does. Or something that you care about. That I think really gets you going and gets you into this flow of like, okay, I see this thing can do cool things. Now I go and, and can expand on this and now I can go and like really think about like, which are the different tools I want, which are the different raw resources and prompts I want. Okay. Now that I have that. Okay. Now do I, what do my evals look like for how I want this to go? How do I optimize my prompts for the evals using like tools like that? This is infinite depth so that you can do. But. Okay. Just start. As simple as possible and just go build a server in like half an hour in the language of your choice and how the model interacts with the things that matter to you. And I think that's where the fun is at. And I think people, I think a lot of what MCP makes great is it just adds a lot of fun to the development piece to just go and have models do things quickly. I also, I'm quite partial, again, to using AI to help me do the coding. Like, I think even during the initial development process, we realized it was quite easy to basically just take all the SDK code. Again, you know, what David suggested, like, you know, pick the language you care about, and then pick the SDK. And once you have that, you can literally just drop the whole SDK code into an LLM's context window and say, okay, now that you know MCP, build me a server that does that. This, this, this. And like, the results, I think, are astounding. Like, I mean, it might not be perfect around every single corner or whatever. And you can refine it over time. But like, it's a great way to kind of like one shot something that basically does what you want, and then you can iterate from there. And like David said, there has been a big emphasis from the beginning on like making servers as easy and simple to build as possible, which certainly helps with LLMs doing it too. We often find that like, getting started is like, you know, 100, 200 lines of code in the last couple of years. It's really quite easy. Yeah. And if you don't have an SDK, again, give the like, give the subset of the spec that you care about to the model, and like another SDK and just have it build you an SDK. And it usually works for like, that subset. Building a full SDK is a different story. But like, to get a model to tool call in Haskell or whatever, like language you like, it's probably pretty straightforward.swyx [00:36:32]: Yeah. Sorry.Alessio [00:36:34]: No, I was gonna say, I co-hosted a hackathon at the AGI house. I'm a personal agent, and one of the personal agents somebody built was like an MCP server builder agent, where they will basically put the URL of the API spec, and it will build an MCP server for them. Do you see that today as kind of like, yeah, most servers are just kind of like a layer on top of an existing API without too much opinion? And how, yeah, do you think that's kind of like how it's going to be going forward? Just like AI generated, exposed to API that already exists? Or are we going to see kind of like net new MCP experiences that you... You couldn't do before?Justin/David [00:37:10]: I think, go for it. I think both, like, I, I think there, there will always be value in like, oh, I have, you know, I have my data over here, and I want to use some connector to bring it into my application over here. That use case will certainly remain. I think, you know, this, this kind of goes back to like, I think a lot of things today are maybe defaulting to tool use when some of the other primitives would be maybe more appropriate over time. And so it could still be that connector. It could still just be that sort of adapter layer, but could like actually adapt it onto different primitives, which is one, one way to add more value. But then I also think there's plenty of opportunity for use cases, which like do, you know, or for MCP servers that kind of do interesting things in and out themselves and aren't just adapters. Some of the earliest examples of this were like, you know, the memory MCP server, which gives the LLM the ability to remember things across conversations or like someone who's a close coworker built the... I shouldn't have said that, not a close coworker. Someone. Yeah. Built the sequential thinking MCP server, which gives a model the ability to like really think step-by-step and get better at its reasoning capabilities. This is something where it's like, it really isn't integrating with anything external. It's just providing this sort of like way of thinking for a model.Justin/David [00:38:27]: I guess either way though, I think AI authorship of the servers is totally possible. Like I've had a lot of success in prompting, just being like, Hey, I want to build an MCP server that like does this thing. And even if this thing is not. Adapting some other API, but it's doing something completely original. It's usually able to figure that out too. Yeah. I do. I do think that the, to add to that, I do think that a good part of, of what MCP servers will be, will be these like just API wrapper to some degree. Um, and that's good to be valid because that works and it gets you very, very far. But I think we're just very early, like in, in exploring what you can do. Um, and I think as client support for like certain primitives get better, like we can talk about sampling. I'm playing with my favorite topic and greatest frustration at the same time. Um, I think you can just see it very easily see like way, way, way richer experiences and we have, we have built them internally for as prototyping aspects. And I think you see some of that in the community already, but there's just, you know, things like, Hey, summarize my, you know, my, my, my, my favorite subreddits for the morning MCP server that nobody has built yet, but it's very easy to envision. And the protocol can totally do this. And these are like slightly richer experiences. And I think as people like go away from like the, oh, I just want to like, I'm just in this new world where I can hook up the things that matter to me, to the LLM, to like actually want a real workflow, a real, like, like more richer experience that I, I really want exposed to the model. I think then you will see these things pop up, but again, that's a, there's a little bit of a chicken and egg problem at the moment with like what a client supported versus, you know, what servers like authors want to do. Yeah.Alessio [00:40:10]: That, that, that was. That's kind of my next question on composability. Like how, how do you guys see that? Do you have plans for that? What's kind of like the import of MCPs, so to speak, into another MCP? Like if I want to build like the subreddit one, there's probably going to be like the Reddit API, uh, MCP, and then the summarization MCP. And then how do I, how do I do a super MCP?Justin/David [00:40:33]: Yeah. So, so this is an interesting topic and I think there, um, so there, there are two aspects to it. I think that the one aspect is like, how can I build something? I think agentically that you requires an LLM call and like a one form of fashion, like for summarization or so, but I'm staying model independent and for that, that's where like part of this by directionality comes in, in this more rich experience where we do have this facility for servers to ask the client again, who owns the LLM interaction, right? Like we talk about cursor, who like runs the, the, the loop with the LLM for you there that for the server author to ask the client for a completion. Um, and basically have it like summarize something for the server and return it back. And so now what model summarizes this depends on which one you have selected in cursor and not depends on what the author brings. The author doesn't bring an SDK. It doesn't have, you had an API key. It's completely model independent, how you can build this. There's just one aspect to that. The second aspect to building richer, richer systems with MCP is that you can easily envision an MCP server that serves something to like something like cursor or win server. For a cloud desktop, but at the same time, also is an MCP client at the same time and itself can use MCP servers to create a rich experience. And now you have a recursive property, which we actually quite carefully in the design principles, try to retain. You, you know, you see it all over the place and authorization and other aspects, um, to the spec that we retain this like recursive pattern. And now you can think about like, okay, I have, I have this little bundle of applications, both a server and a client. And I can add. Add these in chains and build basically graphs like, uh, DAGs out of MCP servers, um, uh, that can just richly interact with each other. A agentic MCP server can also use the whole ecosystem of MCP servers available to themselves. And I think that's a really cool environment, cool thing you can do. And people have experimented with this. And I think you see hopefully more of this, particularly when you think about like auto-selecting, auto-installing, there's a bunch of these things you can do that make, uh, make a really fun experience. I, I think practically there are some niceties we still need to add to the SDKs to make this really simple and like easy to execute on like this kind of recursive MCP server that is also a client or like kind of multiplexing together the behaviors of multiple MCP servers into one host, as we call it. These are things we definitely want to add. We haven't been able to yet, but like, uh, I think that would go some way to showcasing these things that we know are already possible, but not necessarily taken up that much yet. Okay.swyx [00:43:08]: This is, uh, very exciting. And very, I'm sure, I'm sure a lot of people get very, very, uh, a lot of ideas and inspiration from this. Is an MCP server that is also a client, is that an agent?Justin/David [00:43:19]: What's an agent? There's a lot of definitions of agents.swyx [00:43:22]: Because like you're, in some ways you're, you're requesting something and it's going off and doing stuff that you don't necessarily know. There's like a layer of abstraction between you and the ultimate raw source of the data. You could dispute that. Yeah. I just, I don't know if you have a hot take on agents.Justin/David [00:43:35]: I do think, I do think that you can build an agent that way. For me, I think you need to define the difference between. An MCP server plus client that is just a proxy versus an agent. I think there's a difference. And I think that difference might be in, um, you know, for example, using a sample loop to create a more richer experience to, uh, to, to have a model call tools while like inside that MCP server through these clients. I think then you have a, an actual like agent. Yeah. I do think it's very simple to build agents that way. Yeah. I think there are maybe a few paths here. Like it definitely feels like there's some relationship. Between MCP and agents. One possible version is like, maybe MCP is a great way to represent agents. Maybe there are some like, you know, features or specific things that are missing that would make the ergonomics of it better. And we should make that part of MCP. That's one possibility. Another is like, maybe MCP makes sense as kind of like a foundational communication layer for agents to like compose with other agents or something like that. Or there could be other possibilities entirely. Maybe MCP should specialize and narrowly focus on kind of the AI application side. And not as much on the agent side. I think it's a very live question and I think there are sort of trade-offs in every direction going back to the analogy of the God box. I think one thing that we have to be very careful about in designing a protocol and kind of curating or shepherding an ecosystem is like trying to do too much. I think it's, it's a very big, yeah, you know, you don't want a protocol that tries to do absolutely everything under the sun because then it'll be bad at everything too. And so I think the key question, which is still unresolved is like, to what degree are agents. Really? Really naturally fitting in to this existing model and paradigm or to what degree is it basically just like orthogonal? It should be something.swyx [00:45:17]: I think once you enable two way and once you enable client server to be the same and delegation of work to another MCP server, it's definitely more agentic than not. But I appreciate that you keep in mind simplicity and not trying to solve every problem under the sun. Cool. I'm happy to move on there. I mean, I'm going to double click on a couple of things that I marked out because they coincide with things that we wanted to ask you. Anyway, so the first one is, it's just a simple, how many MCP things can one implementation support, you know, so this is the, the, the sort of wide versus deep question. And, and this, this is direct relevance to the nesting of MCPs that we just talked about in April, 2024, when, when Claude was launching one of its first contexts, the first million token context example, they said you can support 250 tools. And in a lot of cases, you can't do that. You know, so to me, that's wide in, in the sense that you, you don't have tools that call tools. You just have the model and a flat hierarchy of tools, but then obviously you have tool confusion. It's going to happen when the tools are adjacent, you call the wrong tool. You're going to get the bad results, right? Do you have a recommendation of like a maximum number of MCP servers that are enabled at any given time?Justin/David [00:46:32]: I think be honest, like, I think there's not one answer to this because to some extent, it depends on the model that you're using. To some extent, it depends on like how well the tools are named and described for the model and stuff like that to avoid confusion. I mean, I think that the dream is certainly like you just furnish all this information to the LLM and it can make sense of everything. This, this kind of goes back to like the, the future we envision with MCP is like all this information is just brought to the model and it decides what to do with it. But today the reality or the practicalities might mean that like, yeah, maybe you, maybe in your client application, like the AI application, you do some fill in the blanks. Maybe you do some filtering over the tool set or like maybe you, you run like a faster, smaller LLM to like filter to what's most relevant and then only pass those tools to the bigger model. Or you could use an MCP server, which is a proxy to other MCP servers and does some filtering at that level or something like that. I think hundreds, as you referenced, is still a fairly safe bet, at least for Claude. I can't speak to the other models, but yeah, I don't know. I think over time we should just expect this to get better. So we're wary of like constraining anything and preventing that. Sort of long. Yeah, and obviously it highly, it highly depends on the overlap of the description, right? Like if you, if you have like very separate servers that do very separate things and the tools have very clear unique names, very clear, well-written descriptions, you know, your mileage might be more higher than if you have a GitLab and a GitHub server at the same time in your context. And, and then the overlap is quite significant because they look very similar to the model and confusion becomes easier. There's different considerations too. Depending on the AI application, if you're, if you're trying to build something very agentic, maybe you are trying to minimize the amount of times you need to go back to the user with a question or, you know, minimize the amount of like configurability in your interface or something. But if you're building other applications, you're building an IDE or you're building a chat application or whatever, like, I think it's totally reasonable to have affordances that allow the user to say like, at this moment, I want this feature set or at this different moment, I want this different feature set or something like that. And maybe not treat it as like always on. The full list always on all the time. Yeah.swyx [00:48:42]: That's where I think the concepts of resources and tools get to blend a little bit, right? Because now you're saying you want some degree of user control, right? Or application control. And other times you want the model to control it, right? So now we're choosing just subsets of tools. I don't know.Justin/David [00:49:00]: Yeah, I think it's a fair point or a fair concern. I guess the way I think about this is still like at the end of the day, and this is a core MCP design principle is like, ultimately, the concept of a tool is not a tool. It's a client application, and by extension, the user. Ultimately, they should be in full control of absolutely everything that's happening via MCP. When we say that tools are model controlled, what we really mean is like, tools should only be invoked by the model. Like there really shouldn't be an application interaction or a user interaction where it's like, okay, as a user, I now want you to use this tool. I mean, occasionally you might do that for prompting reasons, but like, I think that shouldn't be like a UI affordance. But I think the client application or the user deciding to like filter out the user, it's not a tool. I think the client application or the user deciding to like filter out things that MCP servers are offering, totally reasonable, or even like transform them. Like you could imagine a client application that takes tool descriptions from an MCP server and like enriches them, makes them better. We really want the client applications to have full control in the MCP paradigm. That in addition, though, like I think there, one thing that's very, very early in my thinking is there might be a addition to the protocol where you want to give the server author the ability to like logically group certain primitives together, potentially. Yeah. To inform that, because they might know some of these logical groupings better, and that could like encompasses prompts, resources, and tools at the same time. I mean, personally, we can have a design discussion on there. I mean, personally, my take would be that those should be separate MCP servers, and then the user should be able to compose them together. But we can figure it out.Alessio [00:50:31]: Is there going to be like a MCP standard library, so to speak, of like, hey, these are like the canonical servers, do not build this. We're just going to take care of those. And those can be maybe the building blocks that people can compose. Or do you expect people to just rebuild their own MCP servers for like a lot of things?Justin/David [00:50:49]: I think we will not be prescriptive in that sense. I think there will be inherently, you know, there's a lot of power. Well, let me rephrase it. Like, I have a long history in open source, and I feel the bizarre approach to this problem is somewhat useful, right? And I think so that the best and most interesting option wins. And I don't think we want to be very prescriptive. I will definitely foresee, and this already exists, that there will be like 25 GitHub servers and like 25, you know, Postgres servers and whatnot. And that's all cool. And that's good. And I think they all add in their own way. But effectively, eventually, over months or years, the ecosystem will converge to like a set of very widely used ones who basically, I don't know if you call it winning, but like that will be the most used ones. And I think that's completely fine. Because being prescriptive about this, I don't think it's any useful, any use. I do think, of course, that there will be like MCP servers, and you see them already that are driven by companies for their products. And, you know, they will inherently be probably the canonical implementation. Like if you want to work with Cloudflow workers and use an MCP server for that, you'll probably want to use the one developed by Cloudflare. Yeah. I think there's maybe a related thing here, too, just about like one big thing worth thinking about. We don't have any like solutions completely ready to go. It's this question of like trust or like, you know, vetting is maybe a better word. Like, how do you determine which MCP servers are like the kind of good and safe ones to use? Regardless of if there are any implementations of GitHub MCP servers, that could be totally fine. But you want to make sure that you're not using ones that are really like sus, right? And so trying to think about like how to kind of endow reputation or like, you know, if hypothetically. Anthropic is like, we've vetted this. It meets our criteria for secure coding or something. How can that be reflected in kind of this open model where everyone in the ecosystem can benefit? Don't really know the answer yet, but that's very much top of mind.Alessio [00:52:49]: But I think that's like a great design choice of MCPs, which is like language agnostic. Like already, and there's not, to my knowledge, an Anthropic official Ruby SDK, nor an OpenAI SDK. And Alex Roudal does a great job building those. But now with MCPs is like. You don't actually have to translate an SDK to all these languages. You just do one, one interface and kind of bless that interface as, as Anthropic. So yeah, that was, that was nice.swyx [00:53:18]: I have a quick answer to this thing. So like, obviously there's like five or six different registries already popped up. You guys announced your official registry that's gone away. And a registry is very tempting to offer download counts, likes, reviews, and some kind of trust thing. I think it's kind of brittle. Like no matter what kind of social proof or other thing you can, you can offer, the next update can compromise a trusted package. And actually that's the one that does the most damage, right? So abusing the trust system is like setting up a trust system creates the damage from the trust system. And so I actually want to encourage people to try out MCP Inspector because all you got to do is actually just look at the traffic. And like, I think that's, that goes for a lot of security issues.Justin/David [00:54:03]: Yeah, absolutely. Cool. And then I think like that's very classic, just supply chain problem that like all registries effectively have. And the, you know, there are different approaches to this problem. Like you can take the Apple approach and like vet things and like have like an army of, of both automated system and review teams to do this. And then you effectively build an app store, right? That's, that's one approach to this type of problem. It kind of works in, you know, in a very set, certain set of ways. But I don't think it works in an open source kind of ecosystem for which you always have a registry kind of approach, like similar to MPM and packages and PiPi.swyx [00:54:36]: And they all have inherently these, like these, these supply chain attack problems, right? Yeah, yeah, totally. Quick time check. I think we're going to go for another like 20, 25 minutes. Is that okay for you guys? Okay, awesome. Cool. I wanted to double click, take the time. So I'm going to sort of, we previewed a little bit on like the future coming stuff. So I want to leave the future coming stuff to the end, like registry, the, the, the stateless servers and remote servers, all the other stuff. But I wanted to double click a little bit. A little bit more on the launch, the core servers that are part of the official repo. And some of them are special ones, like the, like the ones we already talked about. So let me just pull them up already. So for example, you mentioned memory, you mentioned sequential thinking. And I think I really, really encourage people should look at these, what I call special servers. Like they're, they're not normal servers in the, in the sense that they, they wrap some API and it's just easier to interact with those than to work at the APIs. And so I'll, I'll highlight the, the memory one first, just because like, I think there are, there are a few memory startups, but actually you don't need them if you just use this one. It's also like 200 lines of code. It's super simple. And, and obviously then if you need to scale it up, you should probably do some, some more battle tested thing. But if you're interested, if you're just introducing memory, I think this is a really good implementation. I don't know if there's like special stories that you want to highlight with, with some of these.Justin/David [00:56:00]: I think, no, I don't, I don't think there's special stories. I think a lot of these, not all of them, but a lot of them originated from that hackathon that I mentioned before, where folks got excited about the idea of MCP. People internally inside Anthropik who wanted to have memory or like wanted to play around with the idea could quickly now prototype something using MCP in a way that wasn't possible before. Someone who's not like, you know, you don't have to become the, the end to end expert. You don't have access. You don't have to have access to this. Like, you know. You don't have to have this private, you know, proprietary code base. You can just now extend cloud with this memory capability. So that's how a lot of these came about. And then also just thinking about like, you know, what is the breadth of functionality that we want to demonstrate at launch?swyx [00:56:47]: Totally. And I think that is partially why it made your launch successful because you launch with a sufficiently spanning set of here's examples and then people just copy paste and expand from there. I would also highligh