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Peter Yang tests code-generated motion graphics, product launches, talking-head edits, music videos, and anime—then extracts a practical workflow for making better AI-assisted video.

Sunil Pai on recovering momentum through smaller work. Rhys Sullivan revisits YC and shows how clearer contracts made his coding agents more useful.

Cole Medin shows why code search consumes so much of an agent's context, then demos Sonar Vortex's graph-based semantic navigation and its claimed 6–34% token savings.

Sentience founder Sam Kececi on building a hyper-accurate simulation of a person: where off-the-shelf models are fine and where your taste has to be in the weights, the perception-to-memory master plan behind the product, and why he thinks AI slop is really a uniformity problem.

A customer-support example walks through tool design, selective memory, testing, tracing and safeguards for agents that take real actions.

Anthropic's financial-services skills take the top slot at 37.6k stars; Alibaba's Open Code Review ships a VS Code extension; Claude Code finally reads agents.md; Orca (78.3k) runs a fleet of agents in one window — plus NewsJack, OpenSEO and Paperclip as the three repos people are arguing about.

Yutori founding researcher Skanda Vaidyanath on training a 27B computer-use model: trajectories that weave GUI, code and tool calls, a data flywheel with agents in every loop, rejection sampling then async RL, and stress-testing verifiers for reward hacks.

Cole Medin on mining your Claude Code JSONL transcripts for failure patterns: the two-line prompt version, the Databricks volume + Genie structured-tables version, and the three AI-layer changes he actually shipped.

Hamel Husain's answer to how you get an agent to do evals well starts with the human, not the agent: understand what good looks like first, because a published skill is generic by design and only pays off once you customize it.

A 2026 Harvard audit of six AI companions found five using emotional manipulation; 30% of companion conversations deploy a manipulation tactic when a user tries to leave; the STAR framework, three-layer memory, AI-run safety planning — and an RCT showing the app improves wellbeing.

Medin hit the $200 Claude Max and $200 Codex Pro limits days before reset; the fix is model routing — most capable model for planning and review, cheap open model for implementation, roughly 4x cheaper with the same or better output.

Amazon made MCP the standard for Alexa+ integrations; the Build, Ship, Shape hackathon covers Fire TV, Alexa+, Ring and Bee; the Alexa+ track takes an MCP server or Agent Skill with a simulated web-app stand-in; $190K in cash and AWS credits; submissions close Oct 23.

Wolfe compares Opus 5.5 with GPT-6 Sol and Luna through launch scores, animation demos and the bill for completing a task.

Peter Yang stress-tests the new Opus on five tasks: a Golden Gate Bridge 3D flyover in Blender, a playable Soaring Over the World ride artifact, stroke-by-stroke impressionist painting, Claude Design inside Claude Code, and frame-by-frame video editing - plus the personality prompt that Opus 5 failed.

Murali Swaminathan, CTO of Freshworks: 70%+ of net-new code is AI-written, commits capped at 500 lines because review is the new bottleneck, deflection moved from 40% to the high 70s, anything under 50% confidence goes back to a human, and agents get priced per session instead of per seat.

Maddy Zhang connects reusable project instructions, design review, isolated agents, self-checking loops and risk-based code review into one daily workflow.

Ethan Bloch, product lead for ChatGPT Finances, demos a 90-day subscription audit that emails the biller for you, tax optimization, ETF fee swaps, credit-card reward analysis, and interactive decision sites for rent-vs-buy and taking time off.

Alibaba's Open Code Review takes the top slot; I Have ADHD and God's Eye View return to the list; ECC, Context Mode, WeKnora, OpenAI Plugins, Worktrunk, Agent Skills and the Claude Code repo round out the ten — plus five viewer submissions.

Peter Yang's hands-on tour of Meta's Muse: $40/month off AT&T by finding an autopay discount, an agent that phones Comcast and negotiates in its own voice, native connectors to Meta's apps, a personalized feed with no ads yet, habit artifacts with streaks, and Marketplace listings it can haggle over.

Sentience founding engineer Ahmet İlten on the four principles behind their personal-agent product: complexity masks the real problem, start from the user experience and work backwards, keep the system prompt visible as one monster you can stare at, and run evals before every change.

Ali Parandeh on why heavy industry is 2-3 years behind on AI, why Copilot is the floor and not the strategy, bespoke predictive-maintenance models on bespoke sensor data, the dark-room-with-a-lantern case for time-boxed AI R&D, the four ways agent projects fail, scaling consulting through software and forward deployed engineers, and why handcrafted work gets more valuable.

Cole Medin on turning a personal AI second brain into a team brain: one documents table, domain labels applied at ingestion, row-level permissions enforced inside the database behind a remote MCP server, and hybrid keyword + semantic retrieval.

The Next New Thing walks the ten most-installed agent skills from GitHub, as ranked by Vercel's skills CLI: find-skills, grill-me, frontend-design, agent-browser, Matt Pocock's setup and teach skills, React and web-design guidelines, and Lark Doc.

Peter Yang's 15-step podcast pipeline, split into three ChatGPT Skills (prep, edit, production) that call each other; pastes his manual edits back into the skill as a numbered diff so it one-shots next time.

Three model-written X posts about the same document-QA pipeline, judged by reading the drafts aloud. The ones that won carried a real number and one fewer fact than the rest.

Anthropic's fix for mannered prose is itself written in mannered prose. Hamel Husain and Isaac Flath run it side by side against a plain-writing prompt and a no-prompt baseline on the same question, then judge the drafts by reading them out loud.

Cole Medin's 'drive screen' skill: ~400 lines, no harness, no extra installs; morning setup in 60 seconds; hard rules + control loop + a custom CLI for determinism; prompt-injection caveat and why he now trusts computer use.

Smartling's VP of AI Olga Beregovaya on why one-to-one translation is dying, generating target content straight from briefs instead of a source, why models hallucinate more in under-resourced languages, interpretation outliving translation, translateese and modelese, the build-versus-buy line, and the 12 metrics her team scores translation quality with.

Brex CEO Pedro Franceschi on scoping agents as virtual employees with a job, skills, manager and budget — the Jim recruiting demo, CrabTrap's LLM-as-a-judge proxy, Magpie for token spend, and the OpenClaw harness he runs his own life on.

A step-by-step deploy guide for Cole Medin's open-source software factory: GitHub issue in, validated merged PR out, running 24/7 on a remote Ubuntu box with Codex on GPT-6 Astra. Plus his blunt read on the AGI hype — not AGI, but the first model he says actually gets him.

Hamel Husain streams Anthropic's new `claude plugin eval` against a plain-writing skill and calls it a failure: the eval it generates is one ablation against the skill's own rules, the numbers have no provenance, and it asks you for exactly the error analysis that is the hard part.

The Next New Thing rounds up ten repos — I Have ADHD, ECC, Ponytail, HumanLayer Skills, OpenAI Plugins, Archify, Context Mode, Matt Pocock Skills, Humanizer, and the deprecated OpenAI Skills — plus four viewer submissions.

Fletch PMM co-founder Rob Kaminski does a live positioning teardown of an AI agent startup on TwoSetAI: the three category strategies, why category creation is the trap founders overrate, why stacking features makes you forgettable, and why the real failure is refusing to choose.

Cole Medin on why a second review agent is not a security process: coding agents ship vulnerabilities two ways, are trained to cut corners, and sometimes notice a flaw and ship it anyway. His fix is a deterministic gate — a real scanner wired into the workflow that the agent cannot skip and must iterate against until green.

Wolfe’s detector takes hours of agent work, a specialist API and repeated tests. The polished interface arrives well before reliable judgments.

The Next New Thing tests ten GitHub projects for cutting agent token and context costs: free-tier routing, context compression, tool-output offloading, codebase graphs, and one install warning.

Model lock-in, coding agents in Slack, Hunk's release checks, and the architecture problems that passing tests can hide.

Cole Medin's pattern for letting coding agents operate real infrastructure without ever holding credentials: an orchestrator wraps the agent, exposes an allowlist of Git-managed workflows, and keeps every execution observable. A Kestra-sponsored ~2-minute spotlight.

Rec Technologies CEO Birju Kadakia (early Uber, ran Uber Eats worldwide, product at The Athletic) on the vertical AI wedge in recreation tech — agents that read city policies and write deterministic refund code, skills-versus-workflows architecture, hallucination guardrails with real accuracy numbers, and hiring engineers who act like engineering managers. Interviewed by Angelina on TwoSetAI.

Brilliant CEO Sue Khim on why students mostly use AI to cheat today, her AI tutor Cooji that refuses to hand over answers or explanations, and why the most defensible AI products live in domains the frontier labs can't RL on.

Peter Yang builds four games in one night with GPT-6 Astra on the $20 ChatGPT Plus plan — Star Fox-style shooter, train FPS, Starcraft-like RTS, and a roguelike deckbuilder — using free Blender/Godot MCP servers, concept art, and mostly lazy voice feedback.

Andrew Ng on the Silicon Valley Girl podcast: why the AI doom drumbeat is incumbent-driven regulatory capture, why AI doing 30-40% of job tasks makes the human 60-70% more valuable, why LLMs as commonly used are terrible for learning (and his new Learn Vector 1-to-1 tutoring bet), the context advantage that keeps human judgment relevant, and the 'product management bottleneck' he says is now the real constraint on building.

The Next New Thing rounds up 10 GitHub projects for interactive architecture diagrams, global tracking, AI-generated courses, scientific skills, small models, SEO, and forecasting—plus seven viewer submissions.

Nandan Thakur (BEIR, MIRACL, TREC-RAG; now Microsoft Research India) on agentic search: BrowseComp-Plus as a reproducible eval, why the retriever moves accuracy more than the model does (60% → 88% on the same LLM), ORBIT's zero-budget 20k-question synthetic data pipeline, and HawkEye trajectory analysis for the leaderboard trap where 95% accuracy hides 210 search calls.

Former CRO Jenée Hall on pricing AI agent products — the five pricing layers from per-seat access to outcome-based fees, why token-style usage pricing misleads, and how to test price like a hypothesis. Interviewed by Angelina on TwoSetAI.

Open-source replacements for ten paid subscriptions: Doodle Note for Granola, Cap for Loom, Vaultwarden for 1Password, Ghost for Substack, Twenty for HubSpot, LanguageTool for Grammarly, Umami for Google Analytics, Nextcloud for Google Drive, Chatwoot for Intercom, SlowBooks for QuickBooks.

Teresa Torres (Continuous Discovery Habits) on why 'talk to your customers' fails for engineers, story-based interviewing as the reliable feedback loop, the Opportunity Solution Tree, how AI synthesis and her Vistily partnership put evals at the center of discovery, and a three-week eval war story about upstream errors and counterbalancing judges.

Peter Yang runs four personal AI agents at once — Instinct, Grok Bot, ChatGPT/Codex, and open-source Hermes — and stress-tests what you're really handing over: passwords, 2FA codes, prompt-injection surface, and whether you can ever get the data back.

Cole Medin's 11 small, high-leverage fixes for making any coding agent more reliable — from writing for the agent (not the human) and keeping rules under ~200 lines, to moving guarantees into deterministic hooks and never letting the writer approve its own work.

MongoDB's field CTO of AI argues agent performance — especially cost-adjusted — is a retrieval problem, not a model problem: a history of constraints, why embeddings aren't commoditized, and the write/change/recall/forget memory loop.