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Flight Instructor Suicide: A flight instructor jumps from a plane to leave his student to fend for herself. Could this be the greatest teacher we've ever seen?AI Robot Fails: As they keep trying to get us to work with robots, one of our favorite things is robot fails. Speaking of robot fails, here are a couple of new robot butlers on the horizon.Diarrhea Epidemic: Cyclosporiasis is spreading the country meaning that it's a diarrhea summer.FUCK YOU WATCH THIS!, THE BEAR!, TIP YOUR BARTENDER!, GLASSJAW!, DARYL!, NEW SPECIAL!, SHELL TV!, QR CODE!, SUPERTIPS!, PLATITUDES!, ZEN FELDMAN!, BOOPAC!, TIM TAMS!, FLIGHT INSTRUCTOR!, SUICIDE!, STUDENT PILOT!, NEWS!, MISHAP!, JUMPS FROM PLANE!, TALLER!, BIG!, YOU KNOW WHAT TO DO!, LET'S ROLL!, SIMPSONS!, GAG!, SUICIDE HERO!, KRISPY KREME!, PUT IT IN DRIVE!, BOYZ N DA HOOD!, RICKY!, PHONE THAT TELLS THE FUTURE!, DRIVE!, OLD LADY NEWS!, DISGUISE!, FREAK OUT!, GEEKING!, FALL!, KUNG FU!, DANCING!, BALLET!, GET UP!, GRABBED!, BRUCE LEE!, ROBOT FIGHTS!, AMERICA'S FUNNIEST HOME VIDEOS!, BABIES PUKING!, WALKING THROUGH GLASS!, WEAVE!, SEGWAY!, ISAAC!, STARTUP!, NORI!, REMOTE TASK ROBOTS!, MOP!, RAG!, ROBOT CAMERA!, SKATEBOARDING!, KICKSTARTER!, HORROR UNLEASHED!, DIARRHEA!, INFECTION!, PARASITE!, VEGETABLES!, IMMIGRANTS SHITTING ON LETTUCE!, ICE!, AUSTRALIAN!, GOLDEN CORRAL!, FOREIGNERS!, WORLD CUP!, TOURISTS!, CUT DICK OFF!, SET FIRE!, MUTILATING!, BOOKIE!, PEDEN!, LOAN SHARK!You can find the videos from this episode at our Discord RIGHT HERE!
If you’ve ever felt that nagging sense that the South Australia you were promised at the ballot box isn’t quite the one being delivered, this episode is for you. Stewart Sweeney arrived in Adelaide in 1975 to work inside the Dunstan government’s industrial democracy unit, and fifty years later he’s applying that same insider’s eye to a premier he believes is running the tobacco industry’s old doubt-sowing playbook against the citizens fighting to save the Park Lands. It’s a long, unhurried conversation – fitting, Steve notes, for an episode built around a Negroni “made to be sipped slowly and savoured.” On that note, before the main event, in the SA Drink Of The Week, Alexis and Tina Cattley – the show’s resident cocktail authorities – put Never Never’s Panettone Negroni through its paces, fresh off its win as World’s Best Contemporary Cocktail at the 2026 World Drink Awards in London. And in the Musical Pilgrimage, Adelaide music legend John Schumann returns with the Vagabond Crew to perform and unpack “Rag-tag Extremist Blues,” the song born directly from Malinauskas’ now-infamous put-down of Park Lands protesters. You can navigate episodes using chapter markers in your podcast app. Not a fan of one segment? You can click next to jump to the next chapter in the show. We’re here to serve! The Adelaide Show Podcast: Awarded Silver for Best Interview Podcast in Australia at the 2021 Australian Podcast Awards and named as Finalist for Best News and Current Affairs Podcast in the 2018 Australian Podcast Awards. And please consider becoming part of our podcast by joining our Inner Circle. It’s an email list. Join it and you might get an email on a Sunday or Monday seeking question ideas, guest ideas and requests for other bits of feedback about YOUR podcast, The Adelaide Show. Email us directly and we’ll add you to the list: podcast@theadelaideshow.com.au If you enjoy the show, please leave us a 5-star review in iTunes or other podcast sites, or buy some great merch from our Red Bubble store – The Adelaide Show Shop. We’d greatly appreciate it. And please talk about us and share our episodes on social media, it really helps build our community. Oh, and here’s our index of all episode in one concisepage. Running Sheet: Rag-Tag Extremists 00:00:00 Intro Introduction 00:04:32 SA Drink Of The Week There SA Drink Of The Week this week is Never Never Panettone Negroni. Steve, self-declared “Negroni newbie,” calls in reinforcements for this one: returning cocktail experts Alexis and Tina Cattley, last on the show refereeing a Crows-versus-Power gin showdown. They start with a reference Negroni built by Alexis from Never Never triple juniper gin, a Ruby Bitter in place of Campari, and a small-batch Rosso vermouth from David Franz, north of Adelaide – a drink Steve describes as landing “like a cloud-like pillow,” with a gentle bitter-orange finish. Tina, a self-described “Negroni naysayer” put off by Campari’s bitterness, finds herself won over by how the flavours linger and play off one another. Then comes the star of the segment: Never Never’s bottled Panettone Negroni, fresh from being crowned World’s Best Contemporary Cocktail at the 2026 World Drink Awards in London, and born behind the bar of the brand’s McLaren Vale distillery door before going nationwide. Built from triple juniper gin, a bitter citrus aperitif, sweet vermouth, aged muscat, orange liqueur, rye distillate and vanilla bean, it lands as something else entirely – “IMAX Christmas cake inside my mouth,” in Steve’s words. Alexis reckons it’s the perfect entry point for Negroni sceptics and a ready-made Christmas gift (buy two bottles, he warns – one won’t survive the wait), while Tina, newly vindicated in her Campari aversion, likens it to a sherry or port with all the spiced-cake flavour of the season, minus the traditional Negroni’s lingering bitterness. 00:18:51 Stewart Sweeney Stewart Sweeney has spent fifty years watching power slip loose from democratic control, and he’s watched a lot of it from the inside. In January 1978 he was working inside the Premier’s department when Don Dunstan sacked his own police commissioner over the Salisbury affair – a decision a Royal Commission later vindicated. Nearly fifty years on, Steve posits that the shoe is now on the other foot: where Dunstan acted because he felt deceived by an official, he contends today’s citizens are the ones being deceived, by a premier “savaging our Parklands for a golf tournament and a motorcycle race.” Sweeney’s own arrival in South Australia is a story in itself. A Glaswegian socialist who’d been coaching tennis in upstate New York and found himself unexpectedly unemployed back in Scotland, he stumbled into a tutoring job at the University of Tasmania, landed – almost by accident – in the middle of the Lake Pedder fight, and was introduced to the Democratic Labor Party within his first week in the country by his next-door neighbour, a young Gerard Henderson. From there it was a short hop to Adelaide and Don Dunstan’s Unit for Industrial Democracy, where Sweeney worked alongside economist Phil Bentley on an initiative to bring worker participation into South Australian workplaces – and where, as a footnote that says a great deal about how political communication has evolved, a young Mike Rann landed his very first Adelaide job doing media for the unit. Steve puts it to Sweeney that this early media training was the first real sign of rot in the system – the moment premiers became unreachable, hiding behind stage-managed doorstops instead of being interviewed. Sweeney doesn’t fully disagree, though he tempers it: whatever else came of it, Rann’s genuine gift was for writing speeches fast and communicating big-picture ideas clearly, “sort of decades before AI.” The conversation’s sharpest edge comes when Steve puts the government’s own numbers to Sweeney: the premier’s claim that only 585 of the Park Lands’ roughly 9,000 trees are affected by the disputed golf and motorsport development, against Park Lands management’s markedly different tally. Sweeney names the mechanism he believes is at work – the doubt strategy pioneered by the tobacco industry against the medical evidence on smoking. You don’t need to win the argument, he explains – you just need to plant enough doubt to keep people from acting on their convictions. Layer social media on top of that old tobacco-industry trick, he argues, and doubt becomes exponentially more powerful. Steve adds a live example of his own: an anti-protester Facebook page mocking demonstrators as “NIMBYs,” liked by the premier and half his ministry, that nobody in Labor will admit to running. None of this is abstract history for Sweeney – he’s a park guardian for Park 11 North, and he brings real texture to what’s actually at stake. He unpacks his argument that Adelaide’s Park Lands and squares are treated as “available” land rather than civic inheritance – fenced off for events for weeks at a time, repeatedly re-turfed and re-fenced in what he calls an annual cycle of “fence, event, repair” – and traces the intellectual lineage of the neoliberal thinking now driving those decisions back to a 1949 gathering in Switzerland convened by Friedrich Hayek. He also charts how a shrinking, disciplined leadership pipeline – what he only half-jokingly calls “Farrellism,” after Senator Don Farrell – has narrowed South Australian Labor’s internal debate at exactly the moment its base is most restless, evidenced by the wave of Adelaide residents telling him online, in effect, “I loved Peter Malinauskas, but now I hate Peter Malinauskas.” Sweeney closes on a note that’s more hopeful than the rest of the conversation might suggest. Reaching for the “world systems” thinking that has shaped his outlook for three decades, he reminds listeners that empires, ideologies and premierships all eventually give way to something else – pointing to the radical London reformers who dreamed up the idea of Adelaide in the first place, and to public servant Wainwright, whose ideas quietly transformed the Playford-era economy, as proof that a handful of good ideas, patiently pursued, can still redirect a small regional economy like ours. Further articles by Stewart Sweeney Manufacturing Doubt: Peter Malinauskas’s Political Weapon Think “outside the fence” Power without power: Is Farrellism Labor’s solution or its problem? Productivity is the symptom. Rentier capitalism is the disease 01:45:23 Musical Pilgrimage In the Musical Pilgrimage this week we listen to Rag-Tag Extremist Blues by John Schumann & The Vagabond Crew. The episode closes with John Schumann and the Vagabond Crew performing “Rag-Tag Extremist Blues” – a song that exists purely because Peter Malinauskas dismissed Park Lands protesters with that exact phrase. Schumann, a good deal more measured these days than in his fiery Redgum years, tells Steve he “grabbed it and expanded on it,” deliberately framing his cast of extremists as ordinary South Australians: a retired teacher, a war veteran, taxpaying citizens who’ve never broken a rule in their lives. He’s candid that his fear is less about the golf course – “that boat’s sailed” – and more about the MotoGP development still to come, and about a public whose attention span, he admits, will inevitably wane long before the next election. Streaming and download proceeds go directly to the Adelaide Park Lands Association, and the band will donate part of every ticket sold for their show at The Gov on 29 August to the same cause. Link to all things John Schumann including links to upcoming shows and musicSupport the show: https://theadelaideshow.com.au/listen-or-download-the-podcast/adelaide-in-crowd/See omnystudio.com/listener for privacy information.
Michael interviews new exhibitor Dr. Jeffrey M. Kelly, a full-time biblical counselor and former church planter, and David Driskill, a veteran software developer and Care Assist Pro's CTO, about their platform (careassist-pro.com). They explain that Care Assist Pro was built to address the shortage of soul care in local churches by reducing counseling roadblocks and increasing counselor confidence through secure, HIPAA-compliant tools such as note organization, encrypted communication, built-in video conferencing with transcripts, summaries, and AI-driven feedback on counseling skills over time. They emphasize AI is never the counselor but a tool with biblical guardrails, using a curated RAG knowledge base of about 800 documents (e.g., ACBC, CCF, ABC, Counseling Coalition) and features like notebooks, multilingual outputs, and training “personas” for seminary practicums and skill development.00:00 Conference Introduction01:02 Meet the Guests01:16 Dr Kelly Background01:48 David Driskill Tech Story03:09 How Care Assist Began05:49 Redemptive Use of AI08:31 AI Training Practicum11:19 Notebooks Language Features15:30 Guardrails Not Replacement19:58 Counselor Growth Analytics23:57 Who Its For Pricing26:50 Final Encouragement WrapEpisode MentionsCheck out Care Assist Pro
Thariq Shihipar, a member of Anthropic's Claude Code team (and SPC alum), joins South Park Commons Partner Evan Tana to discuss what it takes to build at the frontier of AI, where the models, tools, and best practices are constantly evolving.Thariq traces his journey from a gaming startup to Anthropic after a chance text about Claude Code, shares why his team stopped using Plan Mode, explains concepts like capability overhang, harness engineering, and the underrated art of writing evals, and discusses why RAG is becoming an anti-pattern. He also breaks down how founders should think about building when the capabilities of AI systems are changing faster than anyone can predict.Thariq Shihipar: https://www.linkedin.com/in/thariqshihipar/ Evan Tana: https://www.linkedin.com/in/evantana/ South Park Commons: https://www.linkedin.com/company/southparkcommons/Apply to SPC: https://www.southparkcommons.com/applyChapters:(00:00:00) - From Startup Founder to Anthropic (00:03:33) - Building Better AI With Claude Code (00:07:21) - The Capability Overhang (00:12:17) - How Anthropic Builds AI Products(00:17:51) - Prompting Claude More Effectively (00:30:58) - How AI Is Changing Product Teams (00:41:11) - Advice for AI Founders
The beauty of maths is that it's completely objective. It's science. It's truth. It's the foundational cornerstone of accumulated knowledge and humanity's long, noble journey towards enlightenment.And so, Ragnar's Maths continues its relentless pursuit of truth by predicting—nay, mathematically proving—what will happen this month:We won't lose.But… ahhh, bugger.This weekend we play a team sitting on the bottom of the ladder. We generally play poorly in situations like this. Anything could happen, and humanity's journey towards enlightenment may yet perform a spectacular U-turn.Ragnar, Sparrow and Orca are here to dive in and reassure the Orange Army.For now, we'll rejoice in the magnificent victory against a side that—for most of the game—was probably the better team.Unfortunately for them, when Clarry's magnificent shoulder arrives at full force, accompanied by a commitment to the football only an overexcited kelpie could rival, chances are you're about to lose.Where have we heard that before?That's right: Rag's Bet.Currently in administration. Possibly insolvent. Almost certainly irresponsible. RIP.However, should you wish to support the cause—and Ragnar's deeply unhealthy relationship with speculative wagering—please visit the Buy Me a Coffee link below.Just kidding. All proceeds will go towards flights to the Grand Final.BECAUSE MARK OUR WORDS: THAT'S A MATHEMATICAL CERTAINTY.We'll see.But until then, we'll Never Surrender.----To get in contact, drop us an email, comment on Spotify or message us on X.We love reviews or ratings.Email: thesquinterspodcast@gmail.comSupport: buymeacoffee.com/thesquintersYouTube: NeverSurrenderByTheSquintersX: TheSquintersInstagram: gws_squintersFB: thesquintersTikTok: the.squinters
Not all work happens in writing. Teams that work with photos, videos, and audio need AI that works for them too. This is why, with Dropbox, you can search within multimedia content for key moments and important information—not just text. In this episode, we talk with Appu Shaji and Hicham Badri, two Dropbox machine learning engineers who are part of the team that makes all of this possible. They explain how multimodal search works—from understanding the context of the initial query, to identifying objects and actions in complex scenes—and how they ensure those models work fast, even at Dropbox-scale. ~ ~ ~ Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck. Our theme song was composed by Doug Stuart. Working Smarter is hosted by Matthew Braga. Thanks for listening!
In 2022, EVONA had eighty staff, a Manchester office, a six-hundred-thousand-pound monthly cost base, and had just spent three hundred thousand pounds flying the whole company to Monaco. Tom Kelly calls it the moment four first-time founders lost perspective.What followed was one of the more honest rebuilds you will hear in recruitment. Contract team gone. Marketing team gone. Management team restructured. Tom moved to the US, took the wheel as sole decision-maker, and rebuilt around two metrics: twenty-five interviews per person per rolling four weeks, and a thirty-day close on every job.Three years on, EVONA is projecting ten million pounds net fee income this year. Three and a half million EBITDA. Thirty-five people. Two inbound client enquiries arrive every single day.Tom Kelly has been in the space sector since 2018. He has never taken outside investment. And last month was the largest in the company's history.On this episode of The RAG Podcast, Tom breaks down exactly how they got there.EVONA is now the dominant name in space sector recruitment. Ninety-five percent US clients. No cold outreach. No single client above nine percent of revenue. A thirty-two day average time to fill on roles that take competitors sixty.Tom Kelly is thirty-nine. He does not have a rigid three-year exit plan, but he does think there is a short window, and he does believe AGI changes the calculus for anyone thinking about an acquisition in this space. What he is building now is the version of the business that is worth something, the lean, high-output, brand-trusted version that took cutting fifty people to find.If you have ever wondered what it actually looks like to rebuild a recruitment business properly after it nearly breaks, this episode has the blueprint.Episode Sponsor: AtlasAdmin is a massive waste of time. That's why there's Atlas, the AI-first recruitment platform built for modern agencies.It doesn't only track CVs and calls. It remembers everything. Every email, every interview, every conversation. Instantly searchable, always available. And now, it's entering a whole new era.With Atlas 2.0, you can ask anything and it delivers. With Magic Search, you speak and it listens. It finds the right candidates using real conversations, not simply look for keywords.Atlas 2.0 also makes business development easier than ever. With Opportunities, you can track, manage and grow client relationships, powered by generative AI and built right into your workflow.Need insights? Custom dashboards give you total visibility over your pipeline. And that's not theory. Atlas customers have reported up to 41% EBITDA growth and an 85% increase in monthly billings after adopting the platform.No admin. No silos. No lost info. Nothing but faster shortlists, better hires and more time to focus on what actually drives revenue.Atlas is your personal AI partner for modern recruiting.Don't miss the future of recruitment. Get started with Atlas today and unlock your exclusive RAG listener offer at https://recruitwithatlas.com/therag/Episode Sponsor: HoxoEvery recruitment founder is investing in LinkedIn, but AI has turned templated posts and outreach into a commodity. When everyone sounds the same, the market stops listening. The recruiters winning now are the ones the market trusts.At Hoxo we help recruitment founders become the most influential name in their niche, using AI to multiply output while trust stays the product. Our clients turn their existing networks into £100K to £300K in new billings within months. Watch the free RAG listener training to see how: https://hubs.ly/Q03lBpYC0
Parce que… c'est l'épisode 0x31B! Shameless plug 19 septembre 2026 - Bsides Montréal 20 au 26 septembre 2026 - BruCON 13 novembre 2026 - DEATHCon 16 au 19 novembre - European Cyber Week 1 au 3 décembre 2026 - Forum INCYBER - Canada 2026 24 et 25 février 2027 - SéQCure 2027 Description Un an de progrès vertigineux Dans cet épisode spécial de Polysécure, l'animateur reçoit Mickael Nadeau pour discuter de l'évolution de l'intelligence artificielle entre deux éditions du concours NorthSec (NSec). Le point de départ est frappant : il y a environ un an et demi, lors de l'avant-dernier NorthSec, les participants commençaient tout juste à expérimenter l'IA en mode « full force », mais les capacités restaient limitées à un simple chatbot capable, au mieux, de générer un script rapide pour automatiser une tâche SSH. Aujourd'hui, le paysage est méconnaissable : les agents autonomes, les architectures de type RAG et les compétences (« skills ») spécialisées ont complètement transformé la façon dont les équipes offensives opèrent. Le point de vue privilégié d'une entreprise défensive Mickael explique que son entreprise, active en cyberdéfense, occupe une position d'observation unique. Grâce à un « mode surveillance » qui laisse les systèmes clients actifs sans bloquer les attaques (plutôt que de les stopper), l'équipe peut observer en temps réel les nouveaux outils et méthodologies utilisés par les équipes de pentest lors de leurs tests d'intrusion. Cette visibilité lui permet presque de dresser un classement informel des équipes selon leur niveau, un peu à la manière d'un « wall of shame » inspiré de DEF CON : certaines équipes s'appuient sur leur réputation sans se renouveler, utilisant des outils et méthodologies dépassés, tandis que d'autres repoussent les limites grâce à une utilisation avancée de l'IA. Du couteau suisse au tireur d'élite spécialisé Un des axes centraux de la discussion porte sur le passage d'agents génériques (le « couteau suisse ») à des sous-agents hautement spécialisés. Mickael illustre ce changement avec l'exemple des injections SQL : au lieu d'utiliser un outil bruyant comme SQLMap qui teste systématiquement toutes les possibilités, on peut désormais entraîner un agent dédié, nourri de toute la documentation existante sur les failles SQL connues, capable de reconnaître un scénario familier et d'aller directement à la solution avec un minimum de requêtes. Le résultat est un attaquant beaucoup plus silencieux, difficile à repérer avec les mécanismes de détection traditionnels basés sur le volume de bruit réseau. Ce changement de paradigme complique considérablement le travail défensif. Les équipes de sécurité, historiquement habituées à trier les faux positifs et à repérer les comportements bruyants et erratiques, doivent maintenant chasser des signaux beaucoup plus subtils, dans une fenêtre de temps réduite à quelques secondes plutôt qu'à plusieurs jours. L'apprentissage machine au service de la défense Contrairement à l'approche probabiliste des grands modèles de langage utilisés côté offensif, l'entreprise de Mickael s'appuie depuis longtemps sur l'apprentissage machine plus déterministe pour détecter les menaces. Ce socle a permis d'atteindre un certain niveau de précision, mais un nouveau défi émerge : la gestion des modèles frontières et des agents eux-mêmes. Beaucoup de clients sont, selon ses mots, « hystériques » face à cette nouvelle réalité, car la majorité des solutions du marché n'ont pas encore intégré une stratégie pour encadrer les agents IA, que ce soit du côté de leurs propres outils ou de ceux fournis par des tiers (pare-feu, plateformes de sécurité, etc.). Des gains opérationnels concrets Au-delà de la détection, les agents transforment aussi les tâches opérationnelles quotidiennes : renouvellement de certificats, requêtes de cache, diagnostics de configuration client complexes — autant d'opérations qui pouvaient auparavant nécessiter plusieurs appels et une expertise pointue peuvent maintenant être gérées via une simple conversation avec un agent, même depuis un téléphone. Mickael raconte un cas concret où un agent a diagnostiqué un problème de pare-feu propre à un client en quelques requêtes, un problème qu'il n'aurait pas su résoudre lui-même immédiatement malgré sa connaissance approfondie du produit. L'avenir des services MDR et la montée des benchmarks La conversation aborde également l'impact sur le marché des services MDR (Managed Detection and Response). Selon Mickael, l'automatisation permise par les agents va permettre à ces équipes de gérer un nombre de clients beaucoup plus important avec les mêmes ressources humaines, tout en relevant le niveau minimal de qualité du secteur. Il prédit également l'émergence de benchmarks formels pour évaluer non seulement les modèles d'IA, mais aussi les fournisseurs de services de sécurité eux-mêmes — un peu comme les leaderboards actuels qui comparent les performances des différents modèles de langage face à des scénarios d'attaque réels. Ces chiffres deviendront un argument de vente central, remplaçant progressivement les discours marketing par des données concrètes et vérifiables. Vers l'automatisation de la conformité Enfin, les deux interlocuteurs discutent du potentiel des agents pour transformer les processus de conformité (comme PCI-DSS), aujourd'hui perçus comme une lourde corvée administrative. Mickael envisage un futur où des « skills » standardisés permettront d'appliquer automatiquement les bonnes pratiques de conformité, réduisant la tentation de sauter des étapes. Il conclut en mentionnant qu'il prépare une présentation avec démonstration en direct pour partager plus en détail cette évolution avec la communauté, dans le but de sensibiliser les organisations à moderniser leur posture défensive à l'ère des agents IA. Collaborateurs Nicolas-Loïc Fortin Mickael Nadeau Crédits Montage par Intrasecure inc Locaux virtuels par Riverside.fm
Across Southeast Asia, generative AI pilots are stalling—not from a lack of model power, but from broken retrieval. Agentic RAG bridges this gap: autonomous agents that verify facts, enforce governance, and execute end-to-end workflows. For CIOs in 2026, this turns fragile experiments into auditable, scalable profit centres. With Gartner warning that 60% of AI projects will be abandoned due to poor data and weak controls, agentic RAG is no longer optional—it is the only practical path from pilot to production. In markets like Singapore, where data residency and compliance are non-negotiable, retrieval intelligence is now the bedrock of ROI.In this PodChats for FutureCIO, Ed Keisling, Chief AI Officer, Progress Software, discusses how CIOs and heads of AI across Southeast Asia, can turn AI pilots and POCs into profit-generating initiatives for enterprises in 2026.What is RAG?Given that most regional AI pilots never scale, what specific architectural weaknesses does agentic RAG fix that traditional RAG or fine-tuning cannot?In markets with fragmented data landscapes—legacy systems, multilingual content, and disparate cloud storage—how does agentic RAG ensure consistent, high-quality retrieval at enterprise scale?What out-of-the-box governance and audit trails does agentic RAG provide to satisfy both local data residency laws (e.g., Singapore's PDPA) and board-level risk controls?For CIOs managing lean teams, how does agentic RAG reduce the operational burden of maintaining retrieval pipelines, monitoring hallucinations, and orchestrating multi-step agent workflows? How can agentic RAG help move beyond isolated use cases (e.g., customer support) toward fully autonomous, end-to-end processes spanning finance, supply chain, and compliance?As agents become more autonomous by 2027, what retrieval strategies will prevent cascading errors or unauthorised actions, and what should CIOs implement today to stay safe?(original 3) How should CIOs in Singapore and across Southeast Asia measure the ROI of retrieval intelligence compared to simply upgrading large language models?For regional enterprises without custom AI stacks, what vendor or open-source scaffolding for agentic RAG offers the fastest path from pilot to profit while preserving data sovereignty?What organisational, data, and leadership shifts must CIOs prioritise over the next 12–18 months to ensure agentic RAG transitions from a technical capability into a sustained source of competitive advantage?
01. Hugel, French Montana, David Guetta, Aidan Martin - Shine (Record Mix) 02. Shouse, Vintage Culture - take me (to the sunrise) (Record Mix) 03. Alok, Innerverse, Frey - Allein Allein (Record Mix) 04. Bob Sinclar, Kiesza - I Can't Wait (Record Mix) 05. Thomas Gold - Pump Up The Jam (Record Mix) 06. Don Diablo, R3hab, Neeka - Disco Marathon 07. Oliver Heldens, Djs From Mars, Jd Davis - Blue Monday (Record Mix) 08. DJ Snake, Bipolar Sunshine - Paradise (Record Mix) 09. Oneil, Kanvise, Ercodes - Smalltown Boy (Record Mix) 10. Avalan Rokston, Alex Caspian - Something to Believe In (Record Mix) 11. Zhu - In the Morning! (Record Mix) 12. Gotlucky, Mysterie, Afro Queen - La La La (Record Mix) 13. Faul, Wad, Pnau - Changes (Record Mix) 14. Rue Jay - I Want Your Love 15. Adam Lambert - Ghost Town (Record Mix) 16. Fedde Le Grand - Got Your Money (Record Mix) 17. Kygo, Ava Max, Tiesto - Whatever (Record Mix) 18. Tujamo, Azteck, Inna - Freak (Record Mix) 19. Sean Paul, Odd Mob - Get Busy 20. Calvin Harris, Benny Blanco - I Found You (Record Mix) 21. DJ Louis - Let Me Blow Ya Mind 22. Topic, A7s - Breaking Me (Record Mix) 23. ONE-T, Ywy, Nika - The Way To Love (Record Mix) 24. New World Sound, J2, Sara Phillips - Outta My Head (Record Mix) 25. Philip George - Wish You Were Mine (Record Mix) 26. Avicii, Dan Tyminski - Hey Brother (Record Mix) 27. Topcover - First Day (Record Mix) 28. Tony Igy, Vicetone - Astronomia (Record Mix) 29. The Chemical Brothers, De Soffer - Go (Record Mix) 30. Lykke Li, The Magician - I Follow Rivers (Record Mix) 31. Garas, Eugenio Fico - Perfect 32. Bebe Rexha, Faithless - New Religion (Record Mix) 33. Disclosure - She's Gone, Dance On 34. DJ Feel, Desmind, Natalie Rise - Stereo Love (Record Mix) 35. Lost Frequencies, Janieck Devy - Reality (Record Mix) 36. Imanbek, Sofia Reyes, Luisa Sonza - NOT U (Record Mix) 37. Sonny Fodera, D.O.D, Poppy Baskcomb - Think About Us (Record Mix) 38. Richard Grey, Kamafro - No Scrubs (Record Mix) 39. Alle Farben - Bad Ideas (Record Mix) 40. Fisher, Flowdan - Boost Up (Record Mix) 41. Gamuel Sori, Lovespeake, Amice - Us (Record Mix) 42. Shane Codd - Get Out My Head (Record Mix) 43. Regard, Years & Years - Hallucination (Record Mix) 44. Global Deejays, Jenia Smile, Ser Twister - The Sound Of San Francisco (Record Mix) 45. Robin Schulz, David Guetta - On Repeat (Record Mix) 46. Alesso, Sacha - Destiny (Record Mix) 47. Don Diablo - The Way I Are (Record Mix) 48. Gorgon City, Mk - There for You (Record Mix) 49. Killteq, D.hash, Vallhee - I Like It (Record Mix) 50. Arc North, Cour, New Beat Order, Lunis - Faded (Record Mix) 51. Anyma, Ejae - Out Of My Body (Record Mix) 52. Ofenbach, Norma Jean Martine - Overdrive (Record Mix) 53. Afrojack, Aloe Blacc - In My World 54. C-BOOL, Giang Pham - DJ Is Your Second Name (Record Mix) 55. Oneil, Kanvise, Ercodes - Every Single Day (Record Mix) 56. David Zowie - House Every Weekend (Record Mix) 57. Skytech - The Rhythm (Record Mix) 58. Twocolors, Roe Byrne, Amice - Stereo (Record Mix) 59. Crazibiza - Fresh (Record Mix) 60. Relanium, Deen West - Leel Lost (Record Mix) 61. Moby, Blond Ish, Kiko Franco - Natural Blues (Record Mix) 62. Lucas & Steve, Laura White - Are You Ready 63. Aria, NA-NO - Bleu Chanel (Record Mix) 64. Martin Jensen, Fastboy - One Day (Record Mix) 65. Calvin Harris, Rag'n'bone Man - Giant (Record Mix) 66. Vize - Wait (Alibi Blue) (Record Mix) 67. Armin Van Buuren - Es Vedra (Record Mix) 68. Vintage Culture, Gabss - Lost (Record Mix) 69. Bassjackers, KSHMR, Sirah - Memories (Record Mix) 70. Akcent, Sera, Misha Miller - Don't Leave (Kylie) (Record Mix) 71. Junior Jack - Stupidisco (Record Mix) 72. Alok, Ella Eyre, Kenny Dope, Never Dull - Deep Down (Record Mix) 73. Joel Corry, Jennifer Lopez - Get Right 74. Jonas Blue, Jp Cooper - Perfect Strangers (Record Mix) 75. Zerb, Sofiya Nzau, Izzy Bizu - Kumbaya (Record Mix) 76. Sebastian Ingrosso, Tommy Trash, John Martin - Reload (Record Mix) 77. All Things Break - Gravity (Record Mix) 78. Jax Jones, Martin Solveig - All Day & Night (Record Mix) 79. Cassian, Yotto, Da Hool - Love Parade (Record Mix) 80. Daft Punk - Around The World (Record Mix) 81. Bob Sinclar - Cruel Summer (Record Mix) 82. Global Deejays - Sex on the Streets (Record Mix) 83. Clean Bandit, ANNE-MARIE, David Guetta - Cry Baby (Record Mix) 84. Tiesto, Ava Max - The Motto (Record Mix) 85. Bolier, Joe Stone, Voost - Keep This Fire Burning (Record Mix) 86. Basto!, Yves V - Cloud Breaker (Record Mix) 87. Goodboys, Nu Aspect, Avaion - Blindspot (Record Mix) 88. Prospa, Josh Baker, Rahh - You Don't Own Me 89. Oceana, Bodybangers - Endless Summer (Record Mix) 90. Joezi, Lizwi - Amathole (Record Mix) 91. Meduza, James Carter, Elley Duhe, Fast Boy - Bad Memories (Record Mix) 92. Adam Port, Stryv, Malachii, Switch Disco - Move (Record Mix) 93. Gorgon City, Romans - Saving My Life (Record Mix) 94. Eastblock Bitches, Ostblockschlampen - Sunglasses at Night (Record Mix) 95. Felix Jaehn, Shouse - Walk With Me (Record Mix) 96. Sean Finn - Give It to Me (Record Mix) 97. Avicii - Fade into Darkness (Record Mix) 98. Teriyaki Boyz, Hayat - Tokyo Drift (Record Mix) 99. Twocolors - Heavy Metal Love (Record Mix) 100. Lady Gaga, DJ Dark - The Dead Dance (Record Mix) 101. Block & Crown, Lissat - Ocean Cake (Record Mix) 102. Hurts, Purple Disco Machine - Wonderful Life '25 (Record Mix) 103. Robin Schulz, James Blunt - OK (Record Mix) 104. Steve Angello - Me 105. Titov - Philosophy (Record Mix) 106. DJ Louis, Sweetpower - Billie Jean (Record Mix) 107. Pawsa - Too Cool To Be Careless (Record Mix)
Video interview with British singer-songwriter Rory Graham, better known as Rag'n'Bone Man. FaceCulture spoke with Rory about Skunk Anansie, his musical household, starting in hip hop, realizing he could sing, The Rum Committee, personal lyrics, stage fright, No Mother, Human, completing the debut album, a song for his grandmother, surprising people, and a lot more! (20/08/2016) Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.
Law professor Julian Nyarko has drawn attention for his studies using large language models to investigate and improve legal education and explore AI's biases. He hopes AI can become a reliable, always-on legal learning and assistance tool to lower costs and expand access to legal services. In one recent study, he asked a group of law professors to evaluate written answers to student questions. Three-quarters of the time, the professors preferred AI-generated answers to those of their human colleagues. “AI is good at law,” Nyarko says, the challenge now is to use it most effectively, he tells host Russ Altman in this episode of Stanford Engineering's The Future of Everything podcast. Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to thefutureofeverything@stanford.edu. Episode Reference Links: Stanford Profile:Julian Nyarko Connect With Us: Episode Transcripts >>> The Future of Everything Website Connect with Russ >>> Threads / Bluesky / Mastodon Connect with School of Engineering >>> Twitter/X / Instagram / LinkedIn / Facebook Chapters: (00:00:00) Introduction Russ Altman introduces guest Julian Nyarko, a professor of law at Stanford University. (00:02:31) Path into AI and Law How Nyarko's early work led him to legal AI. (00:05:03) Law, Economics, and Computation How Nyarko's training, methods, and self-taught coding shaped his research. (00:07:23) Building the LIFT Lab Why a law professor started a lab. (00:09:06) Evaluating Legal AI How AI raises fundamental questions about what counts as good lawyering. (00:10:22) Improving Legal Services Using AI to work faster, reduce errors, and make informed decisions. (00:10:57) Rethinking Legal Education How AI may change the way future lawyers learn (00:11:51) AI in Office Hours How AI answers law students' questions compared with human professors. (00:15:18) Surprising Results Why AI answers were often preferred (00:16:16) What the Study Shows The findings support AI tutoring, but don't prove AI improves learning. (00:18:48) Limits of One-Shot Answers Why real teaching often depends on dialogue, clarification, and productive struggle. (00:20:59) AI for Social Science How AI can become both an object of study and a tool. (00:22:43) Research Agents Using AI to test claims and make previously impossible research scalable. (00:24:51) Agentic AI in the Lab How Socratic dialogue with AI can sharpen research ideas. (00:26:07) Fairness and Bias How computational tools can be audited for bias and used to audit decision-making. (00:27:29) Discrimination in Models Exploring bias and how it can be reduced. (00:30:16) Disparate Impact How policies and systems disadvantage groups even without explicit intent. (00:32:53) From Evidence to Policy How Nyarko's lab works with stakeholders to surface disparities. (00:34:49) Future In a Minute Rapid-fire Q&A: justice, talent, and the future of legal AI. (00:36:57) Conclusion Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
In this talk, Gustaf Gyllensporre, Senior AI Engineer and PropTech Founder, shares his unconventional career journey from selling Miami real estate to shipping production AI systems. We explore the tactical steps for breaking into the tech space as a self taught developer and how to successfully bypass traditional industry gatekeepers.Links:- @PropTechFounder - https://youtu.be/leXRiJ5TuQo?si=ymK03qKVEC7hAt9N- https://x.com/gostak_ddYou will learn about:- The strategic approach to crafting an AI engineering resume that actually gets noticed by hiring managers.- Why building another generic RAG chatbot might be hurting your portfolio and the specific high impact projects you should build instead.- The massive difference between interviewing for AI roles at dynamic startups versus traditional big tech companies.- How to leverage open source contributions to prove your technical mastery without a computer science degree.- The surprisingly simple networking tactics and developer ambassador programs that can unlock exclusive job opportunities.- Actionable ways to improve the critical social and communication skills that most developers completely ignore.TIMECODES:00:00 AI Engineering Field Guide05:01 Self Taught AI Engineer Pivot09:39 CPython Open Source Contributions13:49 Tech YouTube Channel Growth18:26 AI Engineer Resume Optimization22:44 AI Engineering Portfolio Projects29:23 Startup vs Big Tech Interviews33:47 Open Source AI Project Ideas38:55 Building Deep Research AI Agents42:52 Landing Your First AI Job46:48 Tech Networking Strategies51:16 Technical Project Demo Videos54:53 Self Taught Developer Mistakes58:53 Soft Skills for Software EngineersConnect with Gustaf- Linkedin - https://www.linkedin.com/in/gustaf-g/Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/
Many people in this country don't have access to reliable financial guidance — so they're increasingly asking a free chatbot instead. The problem? A general-purpose model trained on Reddit threads inherits Reddit's appetite for risk, and now that skew shows up in the conversations people are having about their money. In this new episode of One Vision Podcast, Alisha Chowdhury, Founder of Kiro Money, joins Theodora Lau to argue that the same technology, pointed with different intentions, can do the opposite: close the advice gap instead of widening it.Alisha traces the origin of Kiro back to a socioeconomically diverse Bangladeshi community in New Orleans — the "aunties and uncles" who taught her immigrant parents how to navigate an unfamiliar financial system — and to the University of Pennsylvania, where she saw real wealth and learned how it's built for the first time. After years as an investor at Vanguard and in private equity, she left for an MBA in London and started building the earliest version of Kiro: a low-code, RAG-based coach fed only sources she trusted. "Not garbage in."Today Kiro is an embedded financial intelligence layer that lets a bank or investing platform drop a context-aware AI coach directly into its own app, so users get guidance where their data and their relationship already live. A conversation about financial inclusion, the advice gap, and what it takes to build AI for money that people can actually trust. Because when it comes to money, trust has to be earned — and tested.
SUMMARY: While we spend a lot of time discussing AI models, we don't always spend enough time on the challenges of managing the unstructured data used to train, tune, and enable those models. SHOW: 1043SHOW TRANSCRIPT: The Enterprise AI Show #1043 TranscriptSHOW VIDEO: https://youtu.be/OAqnuhorMJ4SHOW SPONSORS:Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:Topic 1 - Welcome to the show. Tell us a bit about your background and where you focus today at NasuniTopic 2 - We've spent two years talking about models. Are we finally entering the era where the biggest differentiator is data quality rather than model quality?Topic 3 - When customers inventory their AI-ready data, what surprises them most?Topic 4 - Where is the intersection of file data, metadata, and RAG systems that augment a company's AI experience with their own data?Topic 5 - People talk about AI governance, but isn't most AI governance actually data governance?Topic 6 - Are today's enterprise file systems designed for machine consumers (AI Agents) instead of human consumers?Topic 7 - What are the economics of data, in your world, as it relates to AI?Topic 8 - What's next for enterprise file platforms?FEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow
The "which is smarter" question is dead. Both models are good enough that the right question is which one does the specific thing you need better. This episode breaks down where each one wins for actual work. The short version. Claude wins on writing quality, instruction-following, long-document analysis, and agentic work. ChatGPT wins on image generation, voice, custom GPTs, and ecosystem breadth. Where Claude pulls ahead. For anything client-facing, Claude produces prose that needs less editing, with fewer clichés, better structure, and more controllable tone, which is the single most-cited reason people prefer it for memos, reports, and articles. It also holds detailed constraints better, so when you give it specific headings, a voice, and things to avoid, it sticks to them more faithfully. On the coding and analysis side, Claude leads the reasoning benchmarks (91.3% on GPQA Diamond) and holds a slim edge on SWE-bench Verified, and its context window is the most-cited reason developers switch, with the API tier going up to 1M tokens for long codebases, contracts, and book-length documents. Where ChatGPT pulls ahead. Image generation is not close. ChatGPT generates images natively and Claude cannot generate them at all, so if visuals are in your workflow, that decides it. ChatGPT also browses the web in real time, while Claude does not do that natively, and it integrates directly with Word, Excel, Teams, and Outlook through Microsoft Copilot, which matters if your business already runs on Microsoft 365. For high-volume API work, the flagship cost gap is large: a small internal RAG tool running 10M input and 2M output tokens a month runs roughly $300 on Claude Opus versus $55 on GPT, and it scales from there. Pricing. If you're choosing between Claude Pro and ChatGPT Plus, pick on capability, not price, because they both cost about $20 a month. The one real gap is ChatGPT's cheaper $8 Go tier and its more generous free tier. The move most professionals actually make. The common 2026 setup is ChatGPT for ideation, images, and quick questions, and Claude for the serious writing, editing, long-document analysis, and agentic file work. At about $20 each, running both is roughly $40 a month, which is trivial against the time it saves if AI is core to your job. The AI Career LabBottom line for a service business or agency. If your work is mostly writing, client documents, and code, Claude is the stronger daily driver. If you're producing marketing visuals, doing web research, or living in Microsoft 365, ChatGPT earns its seat. Most people find a clear preference within a week of running both on real work.Topics: Claude vs ChatGPT 2026, best AI for work, AI for small business, AI writing tool, AI for consultants and agencies, Claude Code, ChatGPT vs Claude pricing, long context AI, AI coding model, business AI workflow.Best AI for work 2026, Claude vs ChatGPT for business, AI tool for agencies and freelancers, AI writing and coding assistant, running Claude and ChatGPT together.
In this episode, Ray Cochrane digs into “algorithmic outing,” new research showing that social feeds can infer your sexual orientation before you have consciously come out. He also covers Meta’s privacy-aware AI infrastructure, Alberta’s 466-million-line code scan with Claude, NVIDIA on reinforcement learning, and the many journeys of learning Rust. Along the way, he hits Google DeepMind’s A24 deal, WhatsApp usernames, and scuba-diving cyborg cockroaches. Finally, he looks up with Webb’s puzzling early universe, NASA’s emergency telescope rescue, and a gorgeous aurora from orbit. – 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 with a quick personal update. He hopes listeners had a good holiday weekend, and he shares that he spent his time working his other job at Oregon’s Finest, chatting with people around Portland. Because his Blurbry workweek tends to be solitary, he refills his social meter on the weekends. He then recalls a Saturday night out with coworkers at the Hungry Tiger before turning to the lead story. Algorithmic Outing: When Your Feed Knows Before You Do Cochrane leads with new research from Australia that identifies a phenomenon called “algorithmic outing.” In short, the recommendation systems behind your social feeds can infer your sexual orientation or gender identity and start serving related content before you have worked it out yourself. Importantly, the study is small and qualitative, built on in-depth interviews with twenty LGBTQ+ adults in the Hunter region of New South Wales and published in the journal Gender, Place and Culture. The mechanism is engagement signals: what you like, who you follow, and how long you linger on a post, a metric the industry calls dwell time. Lead researcher Dr. Justin Ellis of the University of Newcastle notes that several participants said the algorithm “knew” they were queer before they did, an experience that felt validating for some but frightening for others in public settings. For Cochrane, the deeper worry is what else that hidden pattern encodes, from upbringing to mental health, and where that data ultimately gets sold. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. Meta’s Blueprint for Privacy-Aware AI Infrastructure Next, Cochrane turns to a sharp engineering piece from Meta on privacy-aware infrastructure. The core challenge is that a system must understand what a piece of data actually is before any privacy rule can protect it. A field named “age,” for example, might describe a person in one place and a cache setting in another. Meta’s answer deploys a large language model only on the genuinely ambiguous cases, then distills what it learns into fixed, human-reviewed rules. The payoff is concrete. According to Meta, those deterministic rules already handle about 85 percent of the traffic, and only the last 15 percent falls back to the model, which costs roughly 400 times more compute. Cochrane loves this edge-case approach. However, he contrasts it sharply with the AI-everywhere software he wrestles with at his weekend job, which he says the heavy AI reliance genuinely makes worse and harder to audit. Alberta Scans 466 Million Lines of Code With Claude This one comes from Anthropic, and it ties directly to Meta’s theme. A team inside Alberta’s Ministry of Technology and Innovation used Claude to scan 466 million lines of code in about twenty hours, a review Anthropic estimates would have taken humans roughly six and a half years. Notably, they ran around fifty AI agents in parallel, essentially an automated red team and blue team probing the systems at once. For Cochrane, this is the good version of AI in production: cleaning up and locking down real systems rather than running the show unsupervised. NVIDIA on Reinforcement Learning for AI Agents On the AI-building side, Cochrane walks through an NVIDIA developer piece on reinforcement learning for agents. Reinforcement learning rewards a model for good behavior rather than showing it the right answer, much like training a dog with treats. Additionally, he clears up a common mix-up. NVIDIA treats RAG, retrieval-augmented generation, as a separate tool: reinforcement learning changes how a model behaves, while RAG changes what facts it can reach. GitHub Retires Two Gemini Models Meanwhile, GitHub is retiring Gemini 2.5 Pro and Gemini 3 Flash across all of Copilot on July 31. The migration paths are Gemini 3.1 Pro and Gemini 3.5 Flash. Cochrane flags it as a sign of the times, since tools that felt brand new a couple of years ago are already getting sunset. He also wonders how quickly today’s “AI-optimized” chips will turn over as the models keep changing. The Many Journeys of Learning Rust One for the programmers, and Cochrane makes no secret of loving Rust. The Rust blog’s Vision Doc series explores how people actually learn the language, which is built around memory safety and its strict borrow checker. Honest themes surface throughout, including “clone guilt,” where beginners refuse to copy anything, and “silent attrition,” the learners who quietly bounce off. His take stands: getting your brain onto a memory-safe language rewires how you approach a problem. Google DeepMind Partners With A24 In an interesting collision of worlds, Google DeepMind is teaming up with A24, the studio behind Hereditary and Everything Everywhere All at Once. The two call it a first-of-its-kind research partnership, with DeepMind researchers and A24 building creative tools shaped by the artists who use them. Cochrane adds a detail worth noting: Google also invested in A24, so this is money on the table, not just a research handshake. For now, though, the announcement stays deliberately vague, with no named films or products. Google’s $1 Million Africa Indie Game Fund Another one from Google, and it is good news for developers. Google is launching an indie games fund for sub-Saharan Africa, a region whose gaming scene is growing about as fast as anywhere. The fund puts up $1 million across ten local studios, each receiving between $50,000 and $200,000 plus mentorship and hands-on support. Applications close at noon UTC on July 31. WhatsApp Usernames Are Here to Reserve WhatsApp is finally moving off phone numbers as your identity. With usernames, someone can start a conversation with you without ever seeing your number. Starting this week, you can reserve the name you want ahead of the full launch later this year. To claim yours, head into Settings, then Account, then Username. Intel Sets Its Q2 Earnings Date Cochrane flags a date worth watching for anyone tracking Intel. The company reports second-quarter results on July 23, right after market close, with an earnings call at 2 p.m. Pacific. Given recent US government investment and a shifting chip landscape, he is curious how the domestic chipmaker is holding up. Your Smartwatch Might Spot Illness Before You Do Shifting to health, Engadget reports that the wearables-plus-AI wave is starting to deliver. These devices excel at catching the moment your body drifts off its own baseline, often the first nudge to get checked out. A 2025 study from Texas A&M and Stanford suggests smartwatches can detect early signs of COVID or the flu within hours of infection. Additionally, Apple Watch’s irregular-rhythm alerts have flagged AFib correctly about 84 percent of the time. Working Memory and Consciousness Here is a heady one from Scientific American, written by philosopher Henry Taylor at the University of Birmingham. Working memory is the mental scratchpad holding whatever you are doing right now. Taylor opens with the doorway effect, that blank moment when you enter a room and forget why. Intriguingly, when information leaves working memory, it seems to leave conscious awareness at the same instant, a link drawing fresh attention across psychology, philosophy, and neuroscience. Scuba-Diving Cyborg Cockroaches Now for the wild one. Scientists have built tiny diving suits that let Madagascar hissing cockroaches survive underwater for up to three hours, while an unequipped roach suffocates in minutes. The 3D-printed suit feeds oxygen through tubes into the insect’s breathing holes, called spiracles, using a chemical generator with no electronics. This lab already steered the roaches with electrodes, so the diving suit is the new trick on top. Researchers pitch it for search and rescue, though Cochrane notes the reality of the spy bug has already arrived. Quantum Time Runs Backward at Los Alamos Next, a genuine brain-bender. Physicists at Los Alamos, led by Luis Pedro García-Pintos, found a way to make a quantum system look like it is running backward in time. To be clear, time is not literally reversing. Precise measurements just make the system’s evolution appear to unfold in reverse. The useful part is energy: measurement itself becomes a resource in what they call a continuous measurement engine. Cochrane admits the paper drifted further from his reality the more he read. Tall Trees Shrug Off Drought A new study in Science overturns some textbook wisdom. For years, the assumption held that taller trees suffer more in drought because they must lift water higher. However, researchers studying dipterocarps in Southeast Asia found that trees topping seventy meters slowed their growth by about the same amount as short ones during the 2023-2024 El Niño drought. The trick is plumbing: a seventy-meter tree grows base vessels roughly twice as wide as a ten-meter tree, so the real driver of drought stress is subtler than raw height. The Energy Department Purges Conservation Pages This next one frustrates Cochrane. The US Department of Energy deleted roughly 6,000 web pages about energy conservation, and the timing is brutal during a record heatwave. The move followed backlash over New York Mayor Zohran Mamdani urging residents to ease strain on the grid. Fortunately, the Internet Archive and its Wayback Machine preserved the pages before they vanished. For Cochrane, deleting that kind of public information simply does not make sense. Webb’s Puzzling New Universe Heading to space, Quanta Magazine explores how the James Webb Space Telescope keeps finding early-universe objects that should not exist. Those include black holes that grew enormous too fast and hundreds of mysterious “little red dots” around 650 million years after the Big Bang. As astrophysicist Rachel Somerville of the Flatiron Institute puts it, scientists have “almost gone from having too many early galaxies to having too many theories.” The hard part now is figuring out which theory is right. NASA’s Emergency Telescope Rescue NASA has a rescue mission underway for the Swift Observatory, a 2004 telescope that studies gamma-ray bursts. Recent solar storms puffed up Earth’s atmosphere, and the added drag has dragged Swift’s orbit down to about 224 miles, low enough to risk burning up this year. To intervene, NASA enlisted Katalyst Space Technologies of Flagstaff, Arizona, whose LINK spacecraft launched Friday. The plan is to boost Swift back up to roughly 373 miles. A Gorgeous Aurora From Orbit Finally, Cochrane closes on something beautiful. ESA shared a stunning aurora captured from orbit, a shimmering green band of light rippling over the planet. If you have a few minutes, it is well worth a look. Cochrane wraps with housekeeping and a thank-you to GoDaddy for two decades of support, then signs off, wishing listeners a wonderful evening. The post Algorithmic Outing: When Your Feed Knows Before You Do #1869 appeared first on Geek News Central.
Learn how Vercel's "self-driving infrastructure" vision pairs with AWS databases to eliminate backend friction, securely cutting Aurora Serverless creation time from minutes to seconds.Topics Include:Hedieh Zandi (Vercel) and Manbeen Kohli (AWS) introduce prompt-to-production sessionVercel powers 18 million developers, maintains Next.js and AI SDKVercel's agentic infrastructure runs on AWS Lambda, CloudFront, and S3AI now generates frontend, APIs, and workflows for small teamsBackend friction remains: credentials, provisioning, database configuration still hardVercel envisions "self-driving infrastructure" that adapts automatically to appsNew AWS partnership brings native Aurora DSQL and Postgres integrationManbeen explains databases now built into Vercel Marketplace and v0Aurora Serverless database creation sped up from minutes to secondsAurora Postgres, DynamoDB, and DSQL scale prototypes without rewritesPre-configured templates help builders start RAG or shopping AI appsDatabase security uses OIDC and IAM tokens, no stored passwordsAWS chosen for agents: low latency, autonomy, one-click simplicityskills.sh gives agents reusable instructions, mirrors AWS Kiro's "powers"v0 lets users build full-stack apps using natural language promptsv0 uses Bedrock models and deploys directly on Vercel infrastructureLive demo: v0 builds restaurant app, provisions database, adds Stripe checkoutDemo ends at AWS console; Rauch quote and hackathon close sessionParticipants:Hedieh Zandi - Product Lead, VercelManbeen Kohli - Director of Product Management, Aurora and RDS Databases, Amazon Web Services See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Both models are good enough that the right question is which one does the specific thing you need better. This episode breaks down where each one wins for actual work. The short version. Claude wins on writing quality, instruction-following, long-document analysis, and agentic work. ChatGPT wins on image generation, voice, custom GPTs, and ecosystem breadth. Where Claude pulls ahead. For anything client-facing, Claude produces prose that needs less editing, with fewer clichés, better structure, and more controllable tone, which is the single most-cited reason people prefer it for memos, reports, and articles. It also holds detailed constraints better, so when you give it specific headings, a voice, and things to avoid, it sticks to them more faithfully. On the coding and analysis side, Claude leads the reasoning benchmarks (91.3% on GPQA Diamond) and holds a slim edge on SWE-bench Verified, and its context window is the most-cited reason developers switch, with the API tier going up to 1M tokens for long codebases, contracts, and book-length documents. Where ChatGPT pulls ahead. Image generation is not close. ChatGPT generates images natively and Claude cannot generate them at all, so if visuals are in your workflow, that decides it. ChatGPT also browses the web in real time, while Claude does not do that natively, and it integrates directly with Word, Excel, Teams, and Outlook through Microsoft Copilot, which matters if your business already runs on Microsoft 365. For high-volume API work, the flagship cost gap is large: a small internal RAG tool running 10M input and 2M output tokens a month runs roughly $300 on Claude Opus versus $55 on GPT, and it scales from there. Pricing. If you're choosing between Claude Pro and ChatGPT Plus, pick on capability, not price, because they both cost about $20 a month. The one real gap is ChatGPT's cheaper $8 Go tier and its more generous free tier. The move most professionals actually make. The common 2026 setup is ChatGPT for ideation, images, and quick questions, and Claude for the serious writing, editing, long-document analysis, and agentic file work. At about $20 each, running both is roughly $40 a month, which is trivial against the time it saves if AI is core to your job. Bottom line for a service business or agency. If your work is mostly writing, client documents, and code, Claude is the stronger daily driver. If you're producing marketing visuals, doing web research, or living in Microsoft 365, ChatGPT earns its seat. Most people find a clear preference within a week of running both on real work.Topics: Claude vs ChatGPT 2026, best AI for work, AI for small business, AI writing tool, AI for consultants and agencies, Claude Code, ChatGPT vs Claude pricing, long context AI, AI coding model, business AI workflow.Best AI for work 2026, Claude vs ChatGPT for business, AI tool for agencies and freelancers, AI writing and coding assistant, running Claude and ChatGPT together.
Healthcare supply chain technology is evolving rapidly, but systemic data fragmentation and operational inefficiencies are moving even faster. In this episode of Supply Chain Now, Scott W. Luton and Scott DeGroot are joined by Mark Holmes of InterSystems and Michael LaRocca of Ready Computing to discuss the shift from legacy networks to proactive, AI-driven decision intelligence. The panel explores the severe consequences of a 10% global surgery cancellation rate due to supply failures, noting that a single canceled orthopedic procedure can cost up to $250,000, while the human toll can be tragic. They address how a lack of visibility leads to duplicate data entry and supply hoarding, outlining a blueprint for a smart data fabric. By leveraging real-time telemetry and agentic AI, healthcare organizations can automate inventory replenishment and resolve disruptions before they impact care. Scott DeGroot concludes by challenging healthcare leaders to audit their tech stacks and bridge the gap between clinical and supply chain data. Jump into the conversation: (00:00) Intro (02:12) Meet InterSystems and Ready Computing leaders (08:28) Ready Computing's 15-year history of system connection (10:43) InterSystems' 48 years of mission-critical data tech (12:29) The life-or-death stakes of healthcare supply chains (17:29) Global surgery cancellations inflict severe financial penalties (21:13) Interoperability standards stitch disconnected databases together (24:43) The "swivel-chair effect" and supply chain hoarding (30:40) Supply Chain Orchestrator powers proactive data orchestration (34:45) Channels360 unifies case management and logistics workflows (40:11) Practical applications of RAG and agentic AI (55:16) Expanding rural healthcare infrastructure strains traditional supply lines Additional Links & Resources: Connect with Mark Holmes: https://www.linkedin.com/in/mark-s-holmes/ Connect with Michael LaRocca: https://www.linkedin.com/in/mlaroccaready Connect with Scott DeGroot: https://www.linkedin.com/in/scott-degroot-4600368/ Learn more about InterSystems: http://www.intersystems.com Learn more about Ready Computing: https://readycomputing.com/ Learn more about our hosts: https://supplychainnow.com/about Learn more about Supply Chain Now: https://supplychainnow.com Watch and listen to more Supply Chain Now episodes here: https://supplychainnow.com/program/supply-chain-now Subscribe to Supply Chain Now on your favorite platform: https://supplychainnow.com/join Work with us! Download Supply Chain Now's NEW Media Kit: https://supplychainnow.com/media-kit/ WEBINAR- Peak Reality Check: What Shippers, Analysts, and AI Models Are Predicting for 2026: https://bit.ly/4aTlsRv WEBINAR- The Future of Supply Chains: Where Talent Meets Technology: https://bit.ly/4uUuxkc WEBINAR- From Volume to Resilience: How Automotive Supply Chains Are Adapting to a New Market Reality: https://bit.ly/4f6SUGA This episode was hosted by Scott Luton and Scott DeGroot and produced by Trisha Cordes, Joshua Miranda, and Amanda Luton. For additional information, please visit our dedicated show page at: https://supplychainnow.com/how-readiness-drives-better-patient-outcomes-1605 The content in this episode, including all audio, videos, visuals, and graphics, is the property of Supply Chain Now and is protected by copyright law. Unauthorized use, reproduction, distribution, modification, or re-uploading of this content in any form is strictly prohibited without explicit written permission from Supply Chain Now.For licensing inquiries or permissions, please contact us at production@supplychainnow.com© 2026 Supply Chain Now. All rights reserved. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Many people in this country don't have access to reliable financial guidance — so they're increasingly asking a free chatbot instead. The problem? A general-purpose model trained on Reddit threads inherits Reddit's appetite for risk, and now that skew shows up in the conversations people are having about their money. In this new episode of One Vision Podcast, Alisha Chowdhury, Founder of Kiro Money, joins Theodora Lau to argue that the same technology, pointed with different intentions, can do the opposite: close the advice gap instead of widening it.Alisha traces the origin of Kiro back to a socioeconomically diverse Bangladeshi community in New Orleans — the "aunties and uncles" who taught her immigrant parents how to navigate an unfamiliar financial system — and to the University of Pennsylvania, where she saw real wealth and learned how it's built for the first time. After years as an investor at Vanguard and in private equity, she left for an MBA in London and started building the earliest version of Kiro: a low-code, RAG-based coach fed only sources she trusted. "Not garbage in."Today Kiro is an embedded financial intelligence layer that lets a bank or investing platform drop a context-aware AI coach directly into its own app, so users get guidance where their data and their relationship already live. A conversation about financial inclusion, the advice gap, and what it takes to build AI for money that people can actually trust. Because when it comes to money, trust has to be earned — and tested.
Si has estado siguiendo mis últimos episodios, sabrás que ando entusiasmado con Hermes, este increíble agente autónomo de Inteligencia Artificial que se ha convertido en mi mano derecha para automatizar todo tipo de tareas en mi servidor. Sin embargo, me he dado cuenta de que he pasado muy de puntillas sobre una de sus características más potentes y que de verdad marca la diferencia cuando queremos exprimir al máximo sus capacidades: los subagentes.
On the Rag, episode 102: Us Weekly- July 3, 2006 | Kevin's Last Chance The Sherman Sister
Think your AI assistant is working perfectly? This episode reveals why most AI breakdowns go completely unnoticed and how these "invisible failures" could be skewing the results we rely on. Fable is Back! Alex Stamos: Anthropic is saying "Amazon's inability to appropriately communicate severity threw our industry into chaos". China's Meituan says its new AI model was trained on domestic chips Chinese A.I. Models Gain Ground on Anthropic and OpenAI Claude Science is Anthropic's newest flagship product Previewing GPT-5.6 Sol: a next-generation model The New York Times Amends Lawsuit Against OpenAI and Microsoft OpenAI and Broadcom Unveil Custom A.I. Chip Design Neon Buys 'Artificial,' a Film About OpenAI, After Amazon Dropped It How AI helped the FBI investigate the White House Correspondents' Dinner attack Anthropic says Alibaba illicitly extracted Claude AI model capabilities Lost books by ancient philosophers recovered from 'unreadable' scrolls Ford had to hire back former engineers to fix mistakes made by its automated systems People have stopped trusting news but not newsrooms SpaceX Showed Investors Prototype of Elon Musk's New AI Device * Gemini Spark, Google's agentic assistant, is now available on Mac Jefferies Warns Memory Prices Will Surge 50% in Q3 2026 and Another 40% in Q4, With No Relief Until 2028 US supreme court rules geofence warrants require constitutional privacy protections Four in five under-16s in Australia using social media despite ban, study shows * Podcasting platform Riverside enters the newsletter publishing game The 'Father of the Internet' is finally retiring Political Bias in AI Om Malik, 1966-2026 Marfa Public Radio Puts You to Sleep My post: California's squandered opportunity Palantir coat Hosts: Leo Laporte, Jeff Jarvis, and Mike Elgan Guest: Chris Potts Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT zscaler.com/security XBOW.com rippling.ai/machines
Think your AI assistant is working perfectly? This episode reveals why most AI breakdowns go completely unnoticed and how these "invisible failures" could be skewing the results we rely on. Fable is Back! Alex Stamos: Anthropic is saying "Amazon's inability to appropriately communicate severity threw our industry into chaos". China's Meituan says its new AI model was trained on domestic chips Chinese A.I. Models Gain Ground on Anthropic and OpenAI Claude Science is Anthropic's newest flagship product Previewing GPT-5.6 Sol: a next-generation model The New York Times Amends Lawsuit Against OpenAI and Microsoft OpenAI and Broadcom Unveil Custom A.I. Chip Design Neon Buys 'Artificial,' a Film About OpenAI, After Amazon Dropped It How AI helped the FBI investigate the White House Correspondents' Dinner attack Anthropic says Alibaba illicitly extracted Claude AI model capabilities Lost books by ancient philosophers recovered from 'unreadable' scrolls Ford had to hire back former engineers to fix mistakes made by its automated systems People have stopped trusting news but not newsrooms SpaceX Showed Investors Prototype of Elon Musk's New AI Device * Gemini Spark, Google's agentic assistant, is now available on Mac Jefferies Warns Memory Prices Will Surge 50% in Q3 2026 and Another 40% in Q4, With No Relief Until 2028 US supreme court rules geofence warrants require constitutional privacy protections Four in five under-16s in Australia using social media despite ban, study shows * Podcasting platform Riverside enters the newsletter publishing game The 'Father of the Internet' is finally retiring Political Bias in AI Om Malik, 1966-2026 Marfa Public Radio Puts You to Sleep My post: California's squandered opportunity Palantir coat Hosts: Leo Laporte, Jeff Jarvis, and Mike Elgan Guest: Chris Potts Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT zscaler.com/security XBOW.com rippling.ai/machines
Think your AI assistant is working perfectly? This episode reveals why most AI breakdowns go completely unnoticed and how these "invisible failures" could be skewing the results we rely on. Fable is Back! Alex Stamos: Anthropic is saying "Amazon's inability to appropriately communicate severity threw our industry into chaos". China's Meituan says its new AI model was trained on domestic chips Chinese A.I. Models Gain Ground on Anthropic and OpenAI Claude Science is Anthropic's newest flagship product Previewing GPT-5.6 Sol: a next-generation model The New York Times Amends Lawsuit Against OpenAI and Microsoft OpenAI and Broadcom Unveil Custom A.I. Chip Design Neon Buys 'Artificial,' a Film About OpenAI, After Amazon Dropped It How AI helped the FBI investigate the White House Correspondents' Dinner attack Anthropic says Alibaba illicitly extracted Claude AI model capabilities Lost books by ancient philosophers recovered from 'unreadable' scrolls Ford had to hire back former engineers to fix mistakes made by its automated systems People have stopped trusting news but not newsrooms SpaceX Showed Investors Prototype of Elon Musk's New AI Device * Gemini Spark, Google's agentic assistant, is now available on Mac Jefferies Warns Memory Prices Will Surge 50% in Q3 2026 and Another 40% in Q4, With No Relief Until 2028 US supreme court rules geofence warrants require constitutional privacy protections Four in five under-16s in Australia using social media despite ban, study shows * Podcasting platform Riverside enters the newsletter publishing game The 'Father of the Internet' is finally retiring Political Bias in AI Om Malik, 1966-2026 Marfa Public Radio Puts You to Sleep My post: California's squandered opportunity Palantir coat Hosts: Leo Laporte, Jeff Jarvis, and Mike Elgan Guest: Chris Potts Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT zscaler.com/security XBOW.com rippling.ai/machines
Think your AI assistant is working perfectly? This episode reveals why most AI breakdowns go completely unnoticed and how these "invisible failures" could be skewing the results we rely on. Fable is Back! Alex Stamos: Anthropic is saying "Amazon's inability to appropriately communicate severity threw our industry into chaos". China's Meituan says its new AI model was trained on domestic chips Chinese A.I. Models Gain Ground on Anthropic and OpenAI Claude Science is Anthropic's newest flagship product Previewing GPT-5.6 Sol: a next-generation model The New York Times Amends Lawsuit Against OpenAI and Microsoft OpenAI and Broadcom Unveil Custom A.I. Chip Design Neon Buys 'Artificial,' a Film About OpenAI, After Amazon Dropped It How AI helped the FBI investigate the White House Correspondents' Dinner attack Anthropic says Alibaba illicitly extracted Claude AI model capabilities Lost books by ancient philosophers recovered from 'unreadable' scrolls Ford had to hire back former engineers to fix mistakes made by its automated systems People have stopped trusting news but not newsrooms SpaceX Showed Investors Prototype of Elon Musk's New AI Device * Gemini Spark, Google's agentic assistant, is now available on Mac Jefferies Warns Memory Prices Will Surge 50% in Q3 2026 and Another 40% in Q4, With No Relief Until 2028 US supreme court rules geofence warrants require constitutional privacy protections Four in five under-16s in Australia using social media despite ban, study shows * Podcasting platform Riverside enters the newsletter publishing game The 'Father of the Internet' is finally retiring Political Bias in AI Om Malik, 1966-2026 Marfa Public Radio Puts You to Sleep My post: California's squandered opportunity Palantir coat Hosts: Leo Laporte, Jeff Jarvis, and Mike Elgan Guest: Chris Potts Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT zscaler.com/security XBOW.com rippling.ai/machines
Carolyn Woodard explores responsible data governance and AI realism with Jim Fruchterman, MacArthur Fellow and founder of Tech Matters, a tech-for-good nonprofit building open source software for the social sector. Jim's work sits at the intersection of two urgent questions every nonprofit is wrestling with right now: what do you owe the people whose data you collect, and how do you make smart decisions about AI without getting swept up in the hype?Jim introduces the Better Deal for Data, a new data governance movement built around seven plain-language commitments nonprofits can make to the communities they serve. The core idea: you don't own the data of the people you serve, tech vendors may be extracting it right now without your knowledge, and a basic data safeguarding policy should be as standard as a child safeguarding policy. He also explains why you can't build a responsible AI governance policy without first getting clear on your data governance.Then the conversation shifts to AI strategy, where Jim draws on decades of experience as an early AI entrepreneur to offer a genuinely grounded take. Gen AI fails in nonprofit program delivery about 80% of the time, and that's still a much better track record than blockchain or the metaverse. Jim and Carolyn discuss:The seven Better Deal for Data commitments and why the average nonprofit can likely adopt them in two hoursHow tech vendors quietly extract and monetize constituent data through survey tools, donor management platforms, and moreWhy data governance and AI governance are inseparable, and why feeding confidential client data into a free AI tool violates bothThe case for nonprofits pooling anonymized data to build better AI models for social impact, with real-world examples from MomConnect and Community SolutionsWhy Jim recommends most nonprofits wait for proven AI products rather than build, and what RAG-based tools are actually delivering results right nowWhy being two or three years behind the for-profit AI curve might actually put nonprofits five to ten years ahead of where they were last yearResources Mentioned:Better Deal for Data – Tech Matters – https://bd4d.orgNonprofit AI Treasure Map – Tech Matters – https://techmatters.org/should-i-be-using-ai-for-this/Technology for Good – Jim Fruchterman – https://fruchterman.org/book/ or at your library or local bookstore! (Free ebook version coming September 2026)Tech Matters Podcast – Jim Fruchterman – https://open.spotify.com/show/17Gptwy6BnxhpBJiPuSNGeMomConnect – South African National Department of Health – https://www.health.gov.za/momconnect/Community Solutions / Built for Zero – https://community.solutionsTech Matters – https://techmatters.org _______________________________Start a conversation :)Register to attend a webinar in real time, and find all past transcripts at https://communityit.com/webinars/email Carolyn at cwoodard@communityit.comon LinkedIn on reddit/r/nonprofitITmanagementon the Community IT websiteThanks for listening.
Think your AI assistant is working perfectly? This episode reveals why most AI breakdowns go completely unnoticed and how these "invisible failures" could be skewing the results we rely on. Fable is Back! Alex Stamos: Anthropic is saying "Amazon's inability to appropriately communicate severity threw our industry into chaos". China's Meituan says its new AI model was trained on domestic chips Chinese A.I. Models Gain Ground on Anthropic and OpenAI Claude Science is Anthropic's newest flagship product Previewing GPT-5.6 Sol: a next-generation model The New York Times Amends Lawsuit Against OpenAI and Microsoft OpenAI and Broadcom Unveil Custom A.I. Chip Design Neon Buys 'Artificial,' a Film About OpenAI, After Amazon Dropped It How AI helped the FBI investigate the White House Correspondents' Dinner attack Anthropic says Alibaba illicitly extracted Claude AI model capabilities Lost books by ancient philosophers recovered from 'unreadable' scrolls Ford had to hire back former engineers to fix mistakes made by its automated systems People have stopped trusting news but not newsrooms SpaceX Showed Investors Prototype of Elon Musk's New AI Device * Gemini Spark, Google's agentic assistant, is now available on Mac Jefferies Warns Memory Prices Will Surge 50% in Q3 2026 and Another 40% in Q4, With No Relief Until 2028 US supreme court rules geofence warrants require constitutional privacy protections Four in five under-16s in Australia using social media despite ban, study shows * Podcasting platform Riverside enters the newsletter publishing game The 'Father of the Internet' is finally retiring Political Bias in AI Om Malik, 1966-2026 Marfa Public Radio Puts You to Sleep My post: California's squandered opportunity Palantir coat Hosts: Leo Laporte, Jeff Jarvis, and Mike Elgan Guest: Chris Potts Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT zscaler.com/security XBOW.com rippling.ai/machines
Send us Fan MailIn case you missed it.....RAG didn't just survive the LLM boom — it became the backbone of how enterprises put AI to work. In this replay, we sit down with Douwe Kiela, the AI researcher who led the team that introduced Retrieval-Augmented Generation in 2020 and went on to co-found Contextual AI. Douwe unpacks whether RAG is here to stay, why most enterprise AI dies in the gap between a great demo and production, how to tame hallucinations, and what actually separates a real "agent" from the buzzword. A clear-eyed conversation on building AI that's grounded, useful, and safe to deploy.00:46 Introducing Douwe Kiela 01:37 RAG - Here to Stay or Go? 06:59 LLMs with Context 08:20 Making AI Successful 10:34 Why Contextual AI? 17:18 LLM versus SLMs 20:28 Speed over Perfection 22:07 Hallucinations 26:02 Making AI Easy to Consume 28:50 Defining an Agent 32:53 Reaching Contextual AI 33:14 The Contrarian View 34:37 The Risks of AI 36:53 For FunLinkedIn: linkedin.com/in/douwekiela Website: contextual.aiWant to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.
Send us Fan MailIn case you missed it.....RAG didn't just survive the LLM boom — it became the backbone of how enterprises put AI to work. In this replay, we sit down with Douwe Kiela, the AI researcher who led the team that introduced Retrieval-Augmented Generation in 2020 and went on to co-found Contextual AI. Douwe unpacks whether RAG is here to stay, why most enterprise AI dies in the gap between a great demo and production, how to tame hallucinations, and what actually separates a real "agent" from the buzzword. A clear-eyed conversation on building AI that's grounded, useful, and safe to deploy.00:46 Introducing Douwe Kiela 01:37 RAG - Here to Stay or Go? 06:59 LLMs with Context 08:20 Making AI Successful 10:34 Why Contextual AI? 17:18 LLM versus SLMs 20:28 Speed over Perfection 22:07 Hallucinations 26:02 Making AI Easy to Consume 28:50 Defining an Agent 32:53 Reaching Contextual AI 33:14 The Contrarian View 34:37 The Risks of AI 36:53 For FunLinkedIn: linkedin.com/in/douwekiela Website: contextual.aiWant to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.
Qdrant Roundtable episode: The Current State of Agentic RetrievalJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguideBig shout-out to Qdrant for the collaboration!// AbstractAI agents are only as good as the information they can find, retrieve, and remember. In this community roundtable with the Qdrant team, we explored the latest advances in agentic memory, vector search, retrieval systems, and production AI architectures.As AI agents move beyond simple chatbots into systems that can reason across large amounts of information, retrieval is becoming one of the most important layers in the AI stack. The discussion covered the real-world challenges of building agents that remember what matters, forget what doesn't, and consistently retrieve the right context at the right time.If you're building AI agents, RAG systems, or production AI applications, this conversation offers practical insights into where retrieval is headed and what it takes to build reliable, scalable agentic systems.// BioEwa SzyszkaEwa is a Developer Relations professional based in San Francisco with a background in Computer Science and Hardware Engineering, passionate about bridging the gap between technology and the developer community. She holds a BSc in Computer Science and an MSc in Electronics, bringing a strong blend of deep technical foundations and communication skills to her work.Dylan CouzonDylan is based in New York City, and he helps developers build better AI applications. He is passionate about AI, programming, open source, and robotics, and enjoys sharing what he's building and learning along the way.Neil KanungoNeil is an experienced professional with expertise in data science, developer relations, and product growth. Currently serving as the Head of Developer Relations at Qdrant, Neil previously held the position of VP of Product Led Growth & Developer Relations at KX, where significant increases in product registration and user activation were achieved. At TIBCO, Neil managed a team focused on enhancing the adoption of TIBCO Spotfire through various initiatives, including tutorial videos and live webinars. With a strong technical background, Neil has developed innovative solutions in analytics, machine learning, and data visualization across multiple roles, including Engineering Data Analyst and Asset Integrity Engineer at Enterprise Products. Neil holds a Bachelor of Science in Radiation Physics from The University of Texas at Austin, a Master of Science in Mechanical Engineering from Texas Tech University, and is pursuing a Master in Applied Data Science from the University of Michigan.Evgeniya SukhodolskayaDeveloper Relations at Qdrant with 8 years of IT experience across software engineering, machine learning, and technical management, and 4 years in Developer Relations. Holds a Master's in Machine Learning, Data Analytics, and Data Engineering. Passionate about NLP, data-centric AI, and the role of vector search in advancing AI technologies.Andrei CristeaAndrei is a Berlin-based Developer Relations Engineer at Qdrant, a prominent open-source vector database. With a Master's degree in Artificial Intelligence from TU Munich, his expertise bridges AI, data infrastructure, and knowledge engineering.Hosted by Demetrios// Related LinksWebsite: https://qdrant.tech/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]
When AI is at its best, the conversations can feel uncanny—almost magical in their accuracy, relevance, and speed. For that you can thank the AI agents that work together behind the scenes to search, reason, and sift through all your content to get you what you need to do your job. We talk with Jongmin Baek and Marta Mendez, two Dropbox machine learning engineers, about building conversational AI that's helpful, useful, and grounded in your team's shared context, so you can spend more time on the work that really matters. ~ ~ ~ Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck. Our theme song was composed by Doug Stuart. Working Smarter is hosted by Matthew Braga. Thanks for listening!
On this episode, Steve and Cody talk about the foundling Din Djar-...er, no, the foundling Edward Langworthy.Sources· Freeman, Michael. “Edward Langworthy: Rags to Riches Patriot.” Freeman's Rag. 22 Jun 2022. . Retrieved 19 May 2026.· Konkle, Burton Alva. “Edward Langworthy.” The Georgia Historical Quarterly 11, no. 2 (1927): 166–70. .· See General Sources page on website for additional sources Hosted on Acast. See acast.com/privacy for more information.
Mike Ames built his first recruitment business from scratch in 1989 with six months of industry experience and two partners. One of them died in a car accident after 18 months. He became MD. He had a breakdown. He rebuilt the business entirely around scalability, removed himself from the centre of it, and sold to a NYSE-listed company for £24 million at the age of 38.He thought that would feel like the finish line. It didn't. Within six months he was so low he could only manage one task a day. His therapist's parting advice: "Well, Michael, just get a job and you'll be fine." So he started again at 38, built a second business, sold that to Harvey Nash in 2017, and spent the next fifteen years working out why most recruitment founders never build something that truly works for them."I'm having to live a lot of my life through my grandchildren because I miss my children. Don't be that guy, right?"This week on The RAG Podcast, Mike Ames breaks down the PLW model: what a Profit, Lifestyle, Wealth recruitment business actually looks like, why the 360 model is structurally flawed, and what the "magic number" is that every founder should know before they spend another year grinding for a goal they have never properly calculated.Mike Ames is not selling a formula for a nine-figure exit. He is arguing for something harder and rarer: a business that runs well, pays you properly, and leaves room for the life you are actually trying to live.If you have ever wondered whether the business you are building is working for you or the other way around, this episode is the one.• • • • • • • • • • • • • • • • • • • • • • • • • •Episode Sponsor: AtlasAdmin is a massive waste of time. That's why there's Atlas, the AI-first recruitment platform built for modern agencies.It doesn't only track CVs and calls. It remembers everything. Every email, every interview, every conversation. Instantly searchable, always available. And now, it's entering a whole new era.With Atlas 2.0, you can ask anything and it delivers. With Magic Search, you speak and it listens. It finds the right candidates using real conversations, not simply keywords.Atlas 2.0 also makes business development easier than ever. With Opportunities, you can track, manage and grow client relationships, powered by generative AI and built right into your workflow.Need insights? Custom dashboards give you total visibility over your pipeline. And that's not theory. Atlas customers have reported up to 41% EBITDA growth and an 85% increase in monthly billings after adopting the platform.No admin. No silos. No lost info. Nothing but faster shortlists, better hires and more time to focus on what actually drives revenue.Atlas is your personal AI partner for modern recruiting.Don't miss the future of recruitment. Get started with Atlas today and unlock your exclusive RAG listener offer at https://recruitwithatlas.com/therag/• • • • • • • • • • • • • • • • • • • • • • • • • •Episode Sponsor: HoxoEvery recruitment founder is investing in LinkedIn. Most can't answer this: how much revenue is it actually bringing in? At Hoxo, we help recruitment founders build predictable revenue systems on LinkedIn. Our clients are turning LinkedIn into £100K-£300K in new billings within months. Fill in the form today at https://hubs.ly/Q03lBpYC0
Helen Gidney, Softcat's Head of Architecture, heads to Las Vegas (in spirit, at least) with Tim Loake from Dell Technologies and Softcat's own Tim Jeans to pick apart the biggest announcements from Dell Technologies World 2026.If you work in enterprise IT, this one is especially worth your time. The conversation covers where AI has actually landed after years of hype, why data sovereignty is reshaping infrastructure decisions, and what the current supply chain situation means for your budget right now.AI off the slide deck and into production. The two Tims and Helen look at which AI applications are generating real business value today, from RAG-based knowledge assistants to coding copilots, and why owning your data is the foundation of any competitive AI strategy.Hybrid cloud and the case for on-premise. Data sovereignty and security concerns are pushing many organisations away from purely public cloud deployments. The episode breaks down what Dell Private Cloud and the newly announced Exoscale storage platform actually offer, including the ability to scale compute and storage independently without locking yourself into a single software vendor.The supply chain problem nobody's budgeted for. AI data centre growth has put serious pressure on memory and drive availability well into 2026 and 2027. The conversation covers how consumption models like Dell APEX can help you secure hardware and lock in pricing before the situation gets worse.Why automation isn't optional. Human error in complex IT environments isn't a training problem, it's a systems problem. The episode makes the case for open-standard automation as the practical answer to operational resilience.The Explain It podcast is released monthly. Subscribe or follow in your app so you don't miss the next one, and if this episode was useful, a review helps other IT leaders find the show.This podcast is produced by The Podcast Coach. Hosted on Acast. See acast.com/privacy for more information.
"We don't see the real costs of AI."That's Jean-Marc Frangos, ex-BT in Silicon Valley, now running AI at Shimizu, one of Japan's "Big 5" contractors, founded in 1804.We sat down with him to unpack how a 220 year old builder actually rolls out AI across a 20,000 person workforce. The playbook, not the hype:✅ Start with your data, not the model✅ 5,000+ staff on one AI platform in ~7 months✅ When the subsidies end, undisciplined teams get hit "like a ton of bricks"
This week on Shared Security, Tom and Kevin sit down with Jay Beale — founder of InGuardians, long-time Black Hat trainer, creator/contributor behind Kubernetes security training, and part of the team behind the DEF CON Kubernetes CTF. Jay shares stories from decades of offensive security work, including the time Tom hired him for a physical penetration test and Jay somehow ended up inside a call center instead of stuck in the lobby. The crew also digs into what makes good security training, why Kubernetes is such a natural platform for both defenders and attackers to understand deeply, and how the DEF CON Kubernetes CTF is designed to be welcoming for both competitors and learners. The episode closes with a practical look at AI infrastructure risk. Jay explains how production AI stacks running on Kubernetes can be attacked like any other cluster — and how modifying a vector database behind a RAG system can turn indirect prompt injection into a persistent, high-impact attack path.** Links mentioned on the show **Jay's Black Hat USA Course: Agentic AI-aided Kubernetes Attack and Defensehttps://blackhat.com/us-26/training/schedule/index.html?day=4daysattue#agentic-ai-aided-kubernetes-attack-and-defense-51318Jay Beale on LinkedInhttps://www.linkedin.com/in/jaybeale/InGuardianshttps://www.inguardians.com/DEF CONhttps://defcon.org/** Watch this episode on YouTube **https://youtu.be/aMHk62dprDA** Become a Shared Security Supporter **Get exclusive access to bonus episodes, listen to new episodes before they are released, receive a monthly shout-out on the show, and get a discount code for 15% off merch at the Shared Security store. Become a supporter today by going to our YouTube channel's membership section: https://www.youtube.com/channel/UCg9CCDIYkDDqwEZ3UYaxjnA/join** Thank you to our sponsors! **SLNTVisit slnt.com to check out SLNT's amazing line of Faraday bags and other products built to protect your privacy. As a listener of this podcast you receive 10% off your order at checkout using discount code "sharedsecurity".** Subscribe and follow the podcast **Subscribe on YouTube: https://www.youtube.com/c/SharedSecurityPodcastFollow us on Bluesky: https://bsky.app/profile/sharedsecurity.bsky.socialFollow us on Mastodon: https://infosec.exchange/@sharedsecurityJoin us on Reddit: https://www.reddit.com/r/SharedSecurityShow/Visit our website: https://sharedsecurity.netSubscribe on your favorite podcast app: https://sharedsecurity.net/subscribeSign-up for our email newsletter to receive updates about the podcast, contest announcements, and special offers from our sponsors: https://shared-security.beehiiv.com/subscribeLeave us a rating and review: https://ratethispodcast.com/sharedsecurityContact us: https://sharedsecurity.net/contact
On the Rag, episode 101: Us Weekly- June 26, 2006 | Brangelina's first days at home with Shiloh The Sherman Sister
Te traigo un tema que me tiene completamente entusiasmado: cómo exprimir todos tus documentos, notas, manuales o archivos locales sin tener que compartirlos con nadie. Te voy a dar una visión general de cómo puedes montar un sistema de recuperación de información para que una inteligencia artificial local se convierta en tu asistente personal definitivo. Todo esto sin salir de tu propia casa, sin APIs de pago y de forma completamente privada.¿Reentrenar o buscar? El gran dilemaPara solucionar esta tremenda limitación, el mundo de la tecnología nos ofrece dos caminos diferentes: hacer un reentrenamiento de un modelo ya existente (lo que conocemos como fine-tuning) o bien montar un sistema RAG (Retrieval-Augmented Generation), que podríamos traducir como generación aumentada por recuperación. En este episodio te desvelo por qué el fine-tuning no es la solución para el común de los mortales: requiere de tarjetas gráficas carísimas, es un proceso lento y estático, y además tus datos privados quedan incrustados dentro del propio modelo, por lo que si se lo entregas a un tercero, estarás regalando tu privacidad. En cambio, un RAG casero es infinitamente más barato, dinámico y respeta tus datos al cien por cien. Imagina que en lugar de obligar al modelo a memorizar toda la biblioteca (que es lo que hace el fine-tuning), le pones a su lado un bibliotecario listísimo que busca la página exacta de los apuntes que necesita antes de responderte. El modelo de lenguaje lee esa página en tiempo real y te contesta basándose únicamente en hechos reales, no en invenciones.La tubería de datos para tu cerebro artificialA lo largo del episodio te explico con todo detalle las piezas que componen esta tubería de datos (o pipeline) que permite hacer magia con tus archivos:La ingestaEl troceado (o chunking)Los embeddings y vectoresLa base de datosLa búsqueda híbridaHerramientas listas para usar y errores que debes evitarSi te da miedo el código, no te preocupes. Te hablo también de alternativas como OpenWeb UI. Y si te va la marcha del desarrollo, te cuento cómo con apenas diez líneas de Python y Streamlit puedes tener una aplicación web propia y completamente funcional.Además, repasamos los tropiezos más habituales que cometemos al empezar en este mundillo, como usar modelos de vectorización que solo entienden inglés para procesar textos en español, no limpiar las cabeceras y pies de página de los PDFs antes de procesarlos, o la importancia crucial de reindexar de forma automática para que tus nuevos documentos estén disponibles al instante.Capítulos del episodio00:00:00 Introducción y de qué va este episodio00:01:54 ¿A qué problema nos enfrentamos con los LLM?00:05:08 Fine-tuning vs. RAG: ¿Cuál es mejor para tus datos?00:08:29 El Pipeline del RAG: De la ingesta a la respuesta00:10:45 ¿Qué es un "embedding" y qué modelos usar con Ollama?00:12:02 El arte de trocear el texto (Chunking)00:13:40 Búsqueda híbrida: Semántica frente a coincidencia exacta00:14:50 Re-ranking: Ordenando los resultados por relevancia00:15:53 El Stack: Ollama, PostgreSQL, pgvector y Podman00:17:25 Alternativas vectoriales: ParadeDB, ChromaDB y Qdrant00:18:36 Manos a la obra con Python y Streamlit00:20:53 OpenWeb UI: La alternativa con RAG integrado y sin código00:21:42 Cómo saber si funciona: El método de las 20 preguntas00:22:51 Errores comunes que debes evitar al montar tu RAG00:23:55 Lo que viene: GraphRAG y RAG agéntico00:24:44 Resumen final y despedidaMás información y enlaces en las notas del episodio
01. Bebe Rexha, David Guetta - Sad Girls (Record Mix) 02. Meduza, James Carter, Elley Duhe, Fast Boy - Bad Memories (Record Mix) 03. Hugel, Ultra Nate - Free (You Got To Live) (Record Mix) 04. Bassjackers, KSHMR, Sirah - Memories (Record Mix) 05. Leony, Calum Scott - Stay (Record Mix) 06. Zerb, The Chainsmokers, Ink - Addicted (Record Mix) 07. Ben Delay - I Never Felt So Right (Record Mix) 08. Anyma, Joji - Beautiful (Record Mix) 09. Avalan Rokston, Alex Caspian - Something to Believe In (Record Mix) 10. Calvin Harris, Dua Lipa - One Kiss (Record Mix) 11. Becky Hill - Outside Of Love (Record Mix) 12. Deorro, Chris Brown - Five More Hours (Record Mix) 13. Melsen, Dwight Steven - One More Night 14. Eric Prydz - Proper Education (Record Mix) 15. Alok, Daecolm, Malou - Unforgettable (Record Mix) 16. Eastblock Bitches, Ostblockschlampen - Sunglasses at Night (Record Mix) 17. Sofiya Nzau, Madism, Robert Miles - Hutia (Record Mix) 18. Modjo - Lady (Hear Me Tonight) (Record Mix) 19. Oliver Heldens, Ian Asher, Sergio Mendes - Mas Que Nada 20. Camelphat, Elderbrook - Cola (Record Mix) 21. Ava Max - Don't Click Play (Record Mix) 22. Joe Stone, Ferreck Dawn - Man Enough (Record Mix) 23. Darude, Glazur, Xm - Sandstorm (Record Mix) 24. Martin Garrix, Ed Sheeran - Repeat It (Record Mix) 25. Showtek, Smack, Sam Gray - Take My Heart Away (Record Mix) 26. Block & Crown - Mr DJ Give Me More 27. Joezi, Lizwi - Amathole (Record Mix) 28. Cassian, Yotto, Da Hool - Love Parade (Record Mix) 29. Duke Dumont - Won't Look Back (Record Mix) 30. Sevdaliza, Yseult, Pabllo Vittar, Tiesto - Alibi (Record Mix) 31. Tiesto, Black Eyed Peas - Pump It Louder (Record Mix) 32. Don Diablo, Nelly Furtado - Doing Nothin' 33. Feder, Emmi - Blind (Record Mix) 34. R.i.o., Gonsu, Jenia Smile, Ser Twister - Shine On (Record Mix) 35. Nicky Romero, Barmuda - Fade Away 36. Titov - Philosophy (Record Mix) 37. Semm, Orfa - How Deep Is Your Love (Record Mix) 38. Kungs, Theophilus London - Galaxy (Record Mix) 39. Argy, Omiki - WIND (Record Mix) 40. Relanium, Deen West - Leel Lost (Record Mix) 41. Swanky Tunes, Shapov - Wannabe 42. Ofenbach - Rock It (Record Mix) 43. Sonny Fodera, D.O.D, Poppy Baskcomb - Think About Us (Record Mix) 44. Sean Finn - Give It to Me (Record Mix) 45. DJ Quba, Sandra K, Ishnlv - Sexy Chick (Record Mix) 46. Doechii, DJ Dark - Anxiety (Record Mix) 47. Tony Igy - Cascade (Record Mix) 48. Shane Codd - Rather Be Alone (Record Mix) 49. Zhu - In the Morning! (Record Mix) 50. Ian Carey, Michelle Shellers, Manyfew, Joe Stone - Keep On Rising (Record Mix) 51. Leo Anderson - One In A Million 52. Block & Crown, Daisy - Mr Vain (Record Mix) 53. Anyma, Ellie Goulding - Hypnotized (Record Mix) 54. Basto!, Yves V - Cloud Breaker (Record Mix) 55. Bob Sinclar, Kiesza - I Can't Wait (Record Mix) 56. Zerb, Sofiya Nzau - Mwaki (Record Mix) 57. Rihanna, Calvin Harris - We Found Love (Record Mix) 58. Alle Farben, Rene Miller - Body Talk (Record Mix) 59. Oceana, Bodybangers - Endless Summer (Record Mix) 60. Kaz James - Sun Is Shining (Record Mix) 61. Alok, Jess Glynne - Summer's Back (Record Mix) 62. Hugel, Topic, Arash, Daecolm - I Adore You (Record Mix) 63. Swedish House Mafia, Pharell - One (Your Name) (Record Mix) 64. Max Oazo - Gimme! Gimme! Gimme! (Record Mix) 65. Jax Jones, Martin Solveig - All Day & Night (Record Mix) 66. Firebeatz, Dubdogz - Give It Up (Record Mix) 67. David Guetta - Family Affair (Dance For Me) (Record Mix) 68. Robin Schulz, Ilsey - Headlights (Record Mix) 69. Dimitri Vegas, Chapter & Verse, Goodboys - Good For You 70. Jerome Robins, Karsten Sollors - Don't Stop The Music (Record Mix) 71. Tiesto, Soaky Siren - Tantalizing (Record Mix) 72. Bobina, Marcus Dielen, Mario Cola - Sweet Dreams (Record Mix) 73. Coldplay, Avicii - A Sky Full of Stars (Record Mix) 74. Pawsa - Too Cool To Be Careless (Record Mix) 75. Pierse, Blair - Bitter Sweet Symphony (Record Mix) 76. Lucas & Steve, Lawrent, Jordan Shaw - End Of Time (Record Mix) 77. Faul, Wad, Pnau - Changes (Record Mix) 78. Alok, Illenium - To The Moon 79. Lola Young, Ted Bear - Messy (Record Mix) 80. Mau P - The Less I Know The Better 81. Dynoro, Gigi D'agostino - In My Mind... (Record Mix) 82. Tayna, Marshmello, Ukay - Si Ai (Record Mix) 83. DJ Antonio, Drama Heroes, DJ Chris Parker, Tomyam - Space (Record Mix) 84. Hurts, Purple Disco Machine - Wonderful Life '25 (Record Mix) 85. Calvin Harris, Rag'n'bone Man - Giant (Record Mix) 86. Trap Mafia House, Red Line, M1ch3l P - Mafia Style 87. Bob Sinclar, Steve Edwards, Fisher - World, Hold On (Record Mix) 88. David Guetta, Sia, Afrojack - Awake Tonight (Record Mix) 89. Shouse, Vintage Culture - take me (to the sunrise) (Record Mix) 90. Bag Raiders - Shooting Stars (Record Mix) 91. Meduza, Henry Camamile - Don't Wanna Go Home (Record Mix) 92. Zhu - Faded (Record Mix) 93. Joel Corry, Pickle, Vula - Stay Together (Baby Baby) (Record Mix) 94. Maruv, Boosin - Drunk Groove (Record Mix) 95. Josh Fawaz - Like a Prayer (Record Mix) 96. Filatov & Karas - Time Won't Wait (Record Mix) 97. DJ Dimixer, Dante, Dmitrii G - Only you (Record Mix) 98. Kddk, Alex Alta - 1&2 (Record Mix) 99. Diplo, Maren Morris - 42 (Record Mix) 100. Yearboox - Graceland (Record Mix) 101. Imany, Ivan Spell, Daniel Magre - You Will Never Know (Record Mix) 102. Argy, Omnya - Aria (Record Mix) 103. Hugel, Imael Angel, Ultra Nate - Movin' To The Sun (Record Mix) 104. Block & Crown - Power Dancer (Record Mix) 105. Albert Brite - Wild (Record Mix) 106. Lost Frequencies, Janieck Devy - Reality (Record Mix)
Au siège de Radio-Canada, à Montréal, Steven Jambot s'est entretenu avec Chloé Sondervorst, réalisatrice qui travaille beaucoup sur les usages éditoriaux de l'intelligence artificielle au sein de ce groupe de média de service public canadien. Il est notamment question de pratiques journalistiques et de la notion de confiance à l'ère de l'IA. L'intelligence artificielle transforme les rédactions, mais pas forcément là où on l'attendait. Pour Chloé Sondervorst, si la transcription automatique fait gagner un temps précieux, l'irruption massive de l'IA générative porte en elle le germe du slop. Cette « bouillie » numérique, produite à bas coût, menace d'enterrer le travail des professionnels de l'information sous une montagne de contenus médiocres. Le péril de la « bouillie » La multiplication des outils de création automatisée crée un paradoxe : l'apparente productivité cache souvent une perte de temps réelle. Chloé Sondervorst alerte sur l'usage de l'IA pour générer des rapports ou des courriels sans supervision humaine : « On a l'apparence d'un travail utile qui en réalité va nous faire perdre du temps parce que la personne qui reçoit cette bouillie [...] va devoir vérifier, va devoir décrypter ». Ce phénomène de dégradation de la qualité pose la question du référencement des contenus synthétiques face à l'information originale produite par des professionnels. Préserver sa « musculature cognitive » L'enjeu n'est pas seulement technique, il est philosophique. Pour l'ancienne étudiante en philosophie, le risque majeur est celui de la dette cognitive. En déléguant trop tôt la réflexion à la machine, le journaliste risque d'atrophier ses propres facultés d'analyse. Elle paraphrase ainsi les propos d'un expert : « On va pas envoyer un robot à la salle de sport à notre place si on veut développer notre musculature.» Et d'ajouter : « Je pense qu'au niveau cognitif, on peut s'appuyer sur cette analogie-là aussi. » Utilisée avec curiosité, l'IA peut servir d'assistance cognitive ou de partenaire de brainstorming, « quelque chose de complémentaire [...] très utile pour nous interroger sur nos propres angles morts ». Le terrain, ultime rempart de l'authenticité Face aux géants technologiques, la souveraineté numérique est devenue un vrai sujet. Dans le service public de l'audiovisuel, le déploiement d'outils d'IA se poursuit, par exemple dans la valorisation des archives. Radio-Canada explore notamment la génération augmentée de récupération (RAG) pour ancrer les réponses de l'IA dans ses propres données certifiées. Pourtant, l'avenir de la profession se jouera peut-être loin des écrans. Pour contrer la méfiance du public, Chloé Sondervorst prône un retour massif au terrain et à l'humain pour « capter les bruissements, les conversations citoyennes qui échappent justement aux algorithmes ». C'est en montrant la fabrication de l'information et en assumant ses doutes que le journaliste (re)deviendra le garant de l'authenticité.
nFactorial Intelligence - еженедельный обзор новостей из мира стартапов и ИИ Рекомендации Наш флагманский онлайн-буткамп по алгоритмам и структурам данных для подготовки к техническим собеседованиям в BigTech начинается 6 июля. Менторы - senior-разработчики в Booking, Google - https://courses.nfactorial.school/algorithms Профессия - AI Engineer: стройте AI-агентов, RAG-системы и production-ready AI-приложения. Начало - 6 июля - https://courses.nfactorial.school/llm Наберите 1500+ на SAT - https://www.instagram.com/nfactorial.admissions/ nFactorial Teens: 2-недельный летний лагерь по вайб-кодингу для школьников в Алматы/Астане (11-16 лет). Цель: создание своего оригинального веб-приложения или веб-игры - https://courses.nfactorial.school/teens Присоединиться к игре nFactorial-сообщества для предсказания результатов матчей ЧМ-2026 - https://www.kicktipp.com/nfactorial2026
Dan Biderman and Jessy Lin, co-founders of Engram, are building a neolab around memory and continual learning, which they call two sides of the same coin. Their contrarian premise: instead of stuffing ever-larger prompts into the context window or bolting on RAG, bake a team's knowledge directly into the model's weights, so it knows your company the way an employee of several years does. The payoff: matching or beating frontier models while consuming up to 100x fewer tokens. Working with partners like Microsoft, Notion, and Harvey, the team draws on roots in computational neuroscience and state-space architectures to attack what they see as the real bottleneck in AI — not raw intelligence, but memory and continual learning. In contrast to the frontier labs' race toward one ever-bigger model and AGI, Dan and Jessy imagine a world where everyone has their own model — privately trained, always learning, and good at the things you actually care about. The real ChatGPT moment for memory, they argue, is the day your model feels like an intern that genuinely got smarter overnight. Hosted by Sonya Huang and Shaun Maguire, Sequoia Capital
This week on Trending in Ed, host Mike Palmer is joined by Trending in Ed all-star Beth Rudden, CEO of Bast AI. From her roots digging in the dirt as an archaeologist to managing a $34 billion division as the Chief Data Officer of IBM Managed Services, Beth brings a deeply grounded, technical perspective to the artificial intelligence conversation. In this wide-ranging and insightful conversation, Mike and Beth skip the typical AI hype to explore what it actually takes to build explainable, trustworthy technology. Beth shares how Bast AI acts as an LLM-agnostic explainability layer—using a unique drinking chocolate analogy to demonstrate how they verify AI data rather than letting models hallucinate plausible narratives. They explore the practical application of using small language models (SLMs) for data enrichment, highlighted by Bast AI's meaningful work with Craig Hospital to translate complex neuro-spine outpatient procedures into accessible languages and analogies. KEY INSIGHTS: • Inverting the Chatbot Approach: Why defining what an AI can talk about is far more effective than building restrictive guardrails. • The Myth of "Human in the Loop": How shifting accountability to overworked humans can become a form of liability laundering. • Microservices vs. Agentic Harnesses: Looking at the risks of natural language agentic systems like Claude Code versus discrete, self-healing tasks. • Cognitive Offloading & Math Education: Why future technical skills should prioritize differential equations and the diversity prediction theorem over simple calculation. • Pattern Recognition vs. Choice: Defining true intelligence through the ability to choose wisely, rather than just matching mathematical patterns. They also cross paths with the Cynefin framework, explain how the human brain conserves energy by only holding two paradoxes at once, and unpack the cultural shifts reshaping modern engineering ethics. Stay ahead of the curve in education and technology! Please like and share this episode with your network, and follow the podcast on Apple Podcasts, Spotify, or your favorite player so you never miss an episode like this one. LINKS: Learn more about Bast AI: https://www.bast.ai Subscribe to Beth's Substack: https://bethrudden.substack.com TIMESTAMPS: 00:00 - Introduction and welcoming Beth Rudden back to the show 01:00 - The drinking chocolate analogy for Explainable AI 03:00 - Beth's lightning-round background: Archaeology to Chief Data Officer at IBM 05:00 - Getting "catfished by AI" and verifying facts with databases 07:00 - Mike on Gemini, RAG applications, and checking AI confabulation 09:00 - Enriched data and Small Language Models (SLMs) at Craig Hospital 12:00 - Epistemic security and inverting conversational technology 14:30 - Liability laundering and the illusion of "human in the loop" 15:30 - Agentic harnesses vs. self-healing microservices 20:00 - Understanding as labor and Conrad Wolfram's three-step math process 22:30 - Future human skills: Differential equations and jelly bean statistics 26:30 - Pattern recognition vs. true intelligence as the ability to choose 29:30 - Neurosymbolic systems and subjectivity in data science 34:30 - Shunting energy: The Cynefin framework and holding paradoxes 38:30 - Healthcare AI scribes and doctor burnout 44:30 - Trust architectures and building tech for the Maintenance Era 47:30 - Cultural devastation and the teleological suspension of ethics 49:00 - Final thoughts and wrapping up with Beth Rudden
At 28, Oliver Paull runs Rec Gen completely alone from his flat in London. £209K in year one at an 81% profit margin. £298K in year two. Past £270K already this year. Close to £800K in under three years, with no staff, no office and no cold client outreach.It did not start that way. In his first recruitment role he was on target to bill £650K and leaving the office to have panic attacks three times a week. He quit to join his best mate Jez Heard's startup, Scale Genesis.Two years later, in a phone call just before Christmas, Jez closed the company down and told him to start his own.His dad, a jeweller, refused to lend him the £3,000 he asked for. "Do you believe enough in yourself to invest your own money to allow you to be successful?" He did. First deal: £27,000 in month one, closed standing at his breakfast bar.On this episode of The RAG Podcast, Ollie breaks down exactly how one recruiter, a podcast and an AI stack replaced an entire agency.Olly is not building headcount. He is building a brand, a system and a lifestyle, and the numbers say it is working.If you have ever wondered whether you could build a serious recruitment business completely alone, this episode has the blueprint.--------------------------------------------------Episode Sponsor: AtlasAdmin is a massive waste of time. That's why there's Atlas, the AI-first recruitment platform built for modern agencies.It doesn't only track CVs and calls. It remembers everything. Every email, every interview, every conversation. Instantly searchable, always available. And now, it's entering a whole new era.With Atlas 2.0, you can ask anything and it delivers. With Magic Search, you speak and it listens. It finds the right candidates using real conversations, not simply look for keywords.Atlas 2.0 also makes business development easier than ever. With Opportunities, you can track, manage and grow client relationships, powered by generative AI and built right into your workflow.Need insights? Custom dashboards give you total visibility over your pipeline. And that's not theory. Atlas customers have reported up to 41% EBITDA growth and an 85% increase in monthly billings after adopting the platform.No admin. No silos. No lost info. Nothing but faster shortlists, better hires and more time to focus on what actually drives revenue.Atlas is your personal AI partner for modern recruiting.Don't miss the future of recruitment. Get started with Atlas today and unlock your exclusive RAG listener offer at https://recruitwithatlas.com/therag/--------------------------------------------------Episode Sponsor: HoxoEvery recruitment founder is investing in LinkedIn, but AI has turned templated posts and outreach into a commodity. When everyone sounds the same, the market stops listening. The recruiters winning now are the ones the market trusts.At Hoxo we help recruitment founders become the most influential name in their niche, using AI to multiply output while trust stays the product. Our clients turn their existing networks into £100K to £300K in new billings within months. Watch the free RAG listener training to see how: https://hubs.ly/Q03lBpYC0
At the World Congress of Anaesthesiologists (WCA) in Marrakech, Morocco, TopMedTalk co-editors In chief, Kate Leslie and Mike Grocott speak with Sydney anaesthesiologist Alwin Chuan, a member of the joint ESRA-ASRA working group on AI, about strengths and limitations of AI for regional anaesthesia and medicine. Chuan explains how generative AI is trained on vast datasets using transformer architectures, why outputs are probabilistic, and how context affects meaning. They discuss privacy and the possibility of personal or creative work being used as a commodity in training, internet "scraping," and the inevitability of hallucinations, including false facts and fabricated citations. The conversation covers bias in training corpora, the need for human curation and reinforcement learning, and mitigation via prompt engineering such as retrieval-augmented generation (RAG), citation/page verification, and confidence estimates. Chuan predicts future advances and the fusion of imaging AI with generative AI for ultrasound guidance, previewing a follow-up episode. -- Join us at Evidence Based Perioperative Medicine (EBPOM) World Congress 2026 in London. Be part of a global conversation as clinicians from around the world gather between 7-9th July at the British Library in London. Three days of evidence-based perioperative medicine, global insights, and expert debate—featuring speakers including Michael Marmot and Ken Rockwood. Register here - EBPOM World Congress 2026
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, creator of the Knowledge Graph Insights Podcast, for their second conversation together. The two cover a wide range of interconnected topics, starting with a correction Larry makes about the true origin of the term "artificial intelligence," tracing it back to the 1956 Dartmouth Conference and its distinction from Norbert Wiener's cybernetics. From there, the conversation moves through the history and structure of knowledge graphs, ontologies, RDF (Resource Description Framework), and the W3C standards process, touching on concepts like the T-box, A-box, and C-box, as well as the 25th anniversary of the Semantic Web paper. Stewart and Larry also dig into the limitations of large language models — particularly around reasoning, confabulation, and what Larry describes as "cognitive surrender" — and why symbolic AI and knowledge engineering may hold answers that the neural network world hasn't fully embraced. The episode also ventures into consciousness, panpsychism, Michael Pollan's ideas, and Stewart's own hands-on experience vibe coding a personal chatbot to replace functionality he feels he's lost with recent changes to Claude. Larry's podcast can be found at kgi.fm.Timestamps00:00 - Stewart introduces Larry Swanson; Larry corrects the record on AI's origin, distinguishing it from Norbert Wiener's cybernetics at the 1956 Dartmouth conference.05:00 - Larry discusses interviewing semantic web paper coauthors on its 25th anniversary; RDF's hidden ubiquity compared to SIM cards powering everything invisibly.10:00 - Knowledge graphs explained through t-box terms, a-box assertions, and Dave McComb's c-box; IKEA's three-layer knowledge graph as a practical example.15:00 - Stewart connects metadata complexity to AI needs; faceted search explained as c-box attributes driving product filtering experiences.20:00 - RDF 1.2 reification standards discussed; W3C's rigorous recommendation process powering governments and enterprises worldwide through collaborative standards.25:00 - Cyc project examined as influential "successful failure"; Pat Hayes bringing description logic into semantic web; LLMs lacking true reasoning capability.30:00 - Epistemological fault lines between human and computer intelligence; cognitive surrender paper reveals no intelligence threshold protects against AI manipulation.35:00 - Stewart's Claude regression problem drives chatbot vibe coding quest; small language models and domain-specific approaches explored as alternatives.40:00 - Consciousness discussion through Michael Pollan's panpsychism lens; language versus cognition disconnect revealing LLMs as pure token-stitching without genuine thought.45:00 - Context graphs as purpose-built knowledge graphs for AI; Stewart's planning agents versus coding agents architecture and ground truth verification problem.50:00 - Docs-as-code versus code-as-docs paradigm shift; knowledge graphs as universal verifiers against validated facts; RDF 1.2 enabling provenance and degrees of certainty.55:00 - Jessica Talisman's Knowledge Graph Academy recommended for onboarding; kgi.fm podcast shared; knowledge representation community needs better abstraction for wider adoption.Key Insights1. The term "artificial intelligence" was not a marketing gimmick but was coined deliberately at the 1956 Dartmouth Conference to distinguish the work of John McCarthy from Norbert Wiener's cybernetics. The two camps represented genuinely different approaches, and the AI label was a form of intentional intellectual branding rather than empty promotion.2. The semantic web, often called the most successful failure in technology history, has quietly embedded itself everywhere despite never achieving its original vision. Technologies like RDF power metadata standards inside every Adobe product and form the invisible backbone of government systems, enterprise data infrastructure, and cultural heritage organizations worldwide.3. Knowledge graphs are best understood as an ontology combined with all the instances that populate it. The distinction between things and strings, popularized by Google in 2012, captures the core idea that knowledge representation is about concepts as distinct from the labels we give them.4. The t-box, a-box, and c-box framework offers a practical model for understanding knowledge architecture. The t-box holds terminology and concepts, the a-box holds assertions about specific instances, and the c-box manages the attributes, taxonomies, and controlled vocabularies that sit between them and enable things like faceted search.5. Large language models produce fluent, convincing output but lack genuine reasoning, epistemological grounding, or judgment. Research on cognitive surrender shows that even people who understand how LLMs work are still susceptible to being misled by their fluency, meaning intelligence and awareness offer no reliable protection against being deceived.6. The gap between language and cognition matters deeply when evaluating AI. Evidence from people with aphasia shows that thinking can occur without language, which suggests LLMs, being purely language-based systems, are missing a fundamental layer of cognition that cannot be recovered through more tokens or better training.7. Knowledge graphs and RDF-based representation are well suited to the problem of verification and grounding in AI systems. Rather than relying on vectorized embeddings of language, a knowledge graph can store validated, provenance-tracked facts with degrees of certainty, making it a natural foundation for building trustworthy AI applications.
It might be easier to mention the bands this week's guest Dan Bonebrake HASN'T played with - his bass playing has graced the likes of Lightworkers, The Honest Liars, Manta Wray, Dashboard Confessional, Grey & Orange, John Ralston, War Generation, Vacant Andys, Enablers, Seville, Quit, Fay Wray, Pivot, Anchorman, Where Fear and Weapons Meet, Cori Elba, The Stiff and on and on... This week, Dan, brings us Washington D.C. post-hardcore iconoclasts Shudder To Think and their 1992 release 'Get Your Goat'. The band's brand of genre-bending 'art rock' might not be everyone's cup of tea, but for those brave enough to climb onboard, it's a decidedly heady ride! Songs discussed in this episode: Animal Wild (Victoria Williams cover) - Shudder To Think; In Time - The Enablers; Nikki - The Stiff; Sleepwalking - Lightworkers; X-French Tee Shirt, Rag, White Page (Live, Germany 1992) - Shudder To Think; Grace - Jeff Buckley; High and Dry - Radiohead; Love Catastrophe - Shudder To Think; Lonely Woman - Ornette Coleman; Gnutheme - All; Shake Your Halo Down, White Page, Goat, Red House, Pebbles - Shudder To Think; Higher And Higher - Craig Wedren; Hot One - Shudder To Think; The Rite Of Spring (Introduction) - Igor Stravinsky; Baby Drop, The Hair Pillow, She Wears He-Harem, Rain-Covered Cat, Funny - Shudder To Think; Kingdom's Coming - Bauhaus; Full Stop - Cori Elba
This Week in Machine Learning & Artificial Intelligence (AI) Podcast
As context windows grow into the millions of tokens, many AI practitioners are questioning whether retrieval-augmented generation (RAG) is still necessary. If modern models can ingest entire libraries of documents, why bother with retrieval at all? In this episode, Alex Bowcut, Head of Engineering at Sphere, explains why the answer depends on the application. Sphere uses AI to automate global tax compliance—an environment where getting the answer right isn't enough. Every conclusion must be backed by the correct legal citation, and every decision must withstand expert review. We explore how Sphere built TRAM (Tax Review and Assessment Model), a production AI system that combines retrieval, reasoning models, legal review workflows, reinforcement learning, and deterministic systems to help tax experts move nearly two orders of magnitude faster while maintaining accuracy. Along the way, we discuss why RAG remains critical in high-stakes domains, how Sphere processes legal and regulatory documents from jurisdictions around the world, retrieval architectures, semantic chunking, dense versus sparse retrieval, expert feedback loops, and the challenges of building AI systems that people can actually trust.
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