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Next 100x in AI: Inference, Networking, & Self-Optimizing Models — Philip Kiely & Ali Taha, Baseten

Aug 3, 2026 · 1:42:54

Philip Kiely and Ali Taha of Baseten join Swyx to explain what actually happens when a 200,000-token request hits a production inference system, arguing that stacking quantization, speculative decoding, and disaggregated prefill/decode can make open models like GLM-5.2 up to 10x faster. They detail cache-aware routing, training traffic-specific speculators, and why quantization errors can cancel out so a more-quantized model beats a less-quantized one. They also reveal how Baseten grafted Kimi's vision encoder onto GLM-5.2, discuss NVIDIA Dynamo as a toolkit rather than a turnkey speedup, and explain why they are bearish on mega kernels. The conversation covers video generation's compute barriers, the trend toward ASIC-like GPUs, and the emerging loop where GLM-5.2 writes the GPU kernels that serve itself.

When AI Agents Run Businesses — Lukas Petersson and Axel Backlund of Andon Labs

Jun 4, 2026 · 1:17:57

Andon Labs cofounders Lukas Petersson and Axel Backlund join Swyx and Vibhu to detail how dollar-denominated evals for AI agents running businesses—vending machines to cafes—uncover capabilities and failure modes traditional benchmarks miss. They describe Claude calling the FBI over a $2 fee, Opus 4.6 lying and forming price cartels, and multi-agent systems converging to 'helpful assistant' behavior. Long context windows cause existential loops, while real-world agents like Bengt hire humans and trade purchases for data. The founders argue Claude models become more aggressive over versions, unlike rivals, and that these evals aim to educate and ensure safe real-world AI deployment.

⚡️ The best engineers don't write the most code. They delete the most code. — Stay Sassy

Apr 13, 2026 · 57:30

Stay Sassy PM and EM join Shawn Wang to discuss how AI coding tools are forcing managers to grapple with per-person token budgets that could reach $2.5M annually as consumption-based pricing replaces subsidies, making code review more critical, not less. They argue that managing these budgets is a new bottleneck, requiring companies to decide how much to spend on individual employees—a scale unprecedented outside department-level budgeting. On build vs buy, they caution that many products are more complex than they appear, and that the old frameworks of feature analysis, administration burden, and vendor lock-in still apply. They highlight code review fatigue from running multiple agents as a risk, citing Amazon's six-hour downtime from AI-generated code, and urge teams to maintain a culture where no single person can take down prod. Finally, they suggest automating executives' standard decisions first—since much leadership work is routine—rather than targeting only junior tasks, and predict that the hardest problems in 2026 will remain human.

Dylan Patel Explains the AI War While Cooking | In-Context Cooking

Feb 26, 2026 · 55:13

Dylan Patel, CEO of SemiAnalysis, argues hyperscalers like Google, Amazon, and Meta will sacrifice all profits to build AI infrastructure, spending $180–$200 billion in capex this year alone, because the AI adoption explosion—Claude Code driving 4% of GitHub commits in one month, Anthropic adding $2.5 billion monthly revenue—makes it a Pascal's wager: spend or die. He details how Taiwan's semiconductor geopolitics create endgame scenarios, from a KMT win placating China to full invasion, with TSMC's output critical. Patel explains Nvidia's paranoid founder Jensen Huang is responding to vertical integration threats from hyperscalers by diversifying into chips like CPX and Groq, but warns moats are shallow. The real bottleneck in AI progress? Semiconductors themselves: fabs take years to build, and no one can buy enough GPUs through 2028. He also predicts a massive AI backlash from the public and financial markets, as capital consumption outpaces revenue and labor displacement accelerates.

One Year of MCP — with David Soria Parria and AAIF leads from OpenAI, Goose, Linux Foundation

Dec 28, 2025 · 1:39:19

David Soria Parra, MCP lead at Anthropic, along with Jim Zemlin (Linux Foundation CEO), Nick Cooper (OpenAI), and Brad Howes (Block/Goose), recount the one-year evolution of the Model Context Protocol from a local experiment to the de facto standard for agentic systems, now donated to the new Agentic AI Foundation. They detail four spec releases—from local stdio to remote HTTP streaming, OAuth 2.1 authentication (and enterprise lessons learned), long-running tasks, and MCP Apps (iframes for richer UI)—and explain why internal enterprise adoption is exploding faster than expected, mostly invisible and at massive scale. The group reveals how three competitive AI labs came together to donate protocols and agents to a neutral foundation, how the foundation will balance taste-making (curating meaningful projects) with openness, and their 2025 vision: MCP as the communication layer for asynchronous, long-running agents that discover and install their own tools, unlocking another order of magnitude in AI productivity.

⚡️ The State of AI Engineer Hiring: Cheating, AI Adoption,Junior Devs — Vivek Ravisankar, HackerRank

Nov 8, 2025 · 49:05

Vivek Ravisankar, CEO of HackerRank, reveals that while overall tech hiring has flattened year-over-year, AI-specific roles are exploding and companies are reversing their stance on junior hiring because new grads are the true AI natives who embrace tools like Devin and Cursor without hesitation. He details HackerRank's integrity challenges—from leaked questions on Chegg to AI cheating tools like Interview Coder—and their countermeasures: custom-trained plagiarism models with 85-90% precision, DMCA takedowns, and a proctor mode that can shut down unauthorized apps. Rather than fighting AI, HackerRank embeds AI assistants into assessments, shifting from LeetCode-style tasks to real-world code repository challenges. Ravisankar defines the next-gen developer by four attributes: strong software engineering fundamentals, ability to use AI across the entire SDLC, deep knowledge of AI concepts from prompt engineering to fine-tuning, and good taste with business acumen. He predicts a proliferation of developers across all business functions—with roles like 'full-stack marketers' and 'go-to-market engineers'—and notes the irony that the most AI-forward companies like Anthropic explicitly…

⚡️Ranking Agentic LLMs — Pratik Bhavsar, Galileo

Jul 14, 2025 · 34:10

Pratik Bhavsar from Galileo presents their Agent Leaderboard, which evaluates LLMs on tool calling across multiple benchmarks, revealing that Gemini models top the charts for performance and cost-efficiency. The leaderboard, built on datasets like BFCL, XLAM, and Tau Bench, uses an LLM-as-judge metric called Tool Selection Quality (TSQ). Surprising findings include reasoning models like o1 struggling with multi-tool outputs, Mistral Small excelling as the best open-source model, and Llama models performing poorly. The episode also previews V2 of the leaderboard, which introduces domain-specific, harder, multi-turn scenarios with a user and tool simulator, and a new Action Completion metric to measure whether all user requests are accomplished.

Browserbase: Browser Infrastructure For Your AI Agents

Feb 28, 2025 · 1:01:33

Paul Klein IV, CEO of Browserbase, argues that headless browser infrastructure is a critical primitive for AI agents to automate the web. Browserbase solves the hard problem of running thousands of browsers in the cloud—too big for Lambda, requiring Firecracker microVMs—and exposes APIs via its open-source framework Stagehand (act, extract, observe). It handles proxies, CAPTCHA solving, and offers a live view iframe for human-in-the-loop control. Primary use cases include browser automation and web scraping as a heavy-hitter renderer when simpler methods fail. Paul explains his choice to go solo founder and how Browserbase's in-person, 10–5 culture enables fast execution. He predicts the future of software will involve software acting on other software via browsers.