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The Next New Thing’s Andrew Warner and Mat Nolen test a collection of GitHub projects aimed at one of the biggest costs in agentic coding: context and token usage. Ten repos get demoed on camera (plus one more flagged in the episode’s resource list), and for most of them Mat reports his own measurement next to the project’s claim. Routing around the bill Free LLM API — stacks the free tiers from multiple providers behind one interface and automatically switches to the next one when a tier runs out. Warner interviewed the founder, who says providers now pitch him for inclusion because they want to be the entry point where users experiment. Ships Mac, Windows, and Android apps. OmniRoute — one endpoint, hundreds of providers, free tier first. Unlike Free LLM API it also routes across your paid providers (Anthropic to OpenAI and on down the list) when a quota runs dry, using the RTK compression engine on the way through. The hosts saved it for the end because it folds most of the other tricks into a single gateway. Compressing what comes back Headroom — a compression layer that sits between your agent and the API and squeezes tool output on the way back: it shortens what you read, not what you write. Mat’s test compressed a file to roughly 37% of its size and he had no complaints — at times it beat the README’s numbers. Magic Compact — a lossless context compressor that swaps a large tool output for a one-line note and moves the payload to disk on the logic that context is expensive and disk is cheap, so move it rather than delete it. Mat ran it eight times and got identical results every time. Low profile at 147 stars, and the developer has paused it to build a successor around agent memory. PX Pipe — the viral experiment: render the context as images and have the model read the picture instead of the text. It worked, but accuracy slipped, especially on older or local models that read images poorly — and the failure mode is a hallucinated context. 7,000+ stars off the back of the X attention alone. LeanCTX — change one import line and compress prompts by dropping low-value filler words. The project claims 40–60% savings; Mat measured 52%. His caveat: you do not get to see exactly which words were thrown away. Keeping context out of the window Context Mode — keeps raw tool output out of the context window entirely, storing it in a local index the model queries when it needs the data. The README claims a 98% reduction; Mat measured around 95%, which he called a hit. Token Optimizer MCP — an MCP server that blocks expensive calls the agent has already made and points it back at what it already read, acting as a short-term memory layer. Watch this one: installing it turns off Claude Code’s trust prompt, which the hosts flag as a production no-no. Graphify — parses a codebase into a knowledge graph so the agent queries the map rather than opening files, and it builds that map with zero model tokens, so you can try it without spending anything. 100,000+ stars and a Y Combinator batch behind it. codebase-memory-mcp — listed in the episode’s resources as a local memory server for codebases; not demoed on camera. Writing less instead of cheaper Ponytail — the jaded senior developer sitting inside your agent: where an overeager junior installs a library and wraps it, this pushes the agent toward what the browser or the codebase already has. Mat measured the cheapest token usage of the batch, and it stayed on par with its claims. The takeaway The pattern across the roundup: most of these savings come from moving work out of the context window — to disk, to a local index, to a graph, or to another provider — rather than from making the model smarter. The two caveats worth remembering are the ones the hosts surfaced themselves: tools that strip context you cannot inspect, and tools that loosen your agent’s permission settings on install. ...

Cole Medin on the permission pattern he uses so AI coding agents can operate real infrastructure without holding the keys — a ~2-minute spotlight, sponsored by Kestra (their 2.0 just shipped; the core stays Apache-2.0). Why trust breaks at the boundary He has used coding agents to write every line of code this year — but still monitors and validates everything The limiter isn’t the model, it’s what the agent can reach: the moment it touches real infrastructure you hand it a cloud key, a database URL, shell access — everything, including the ability to wipe the database The fix: an orchestrator between the agent and the infra Wrap the coding agent in an orchestrator that gives it workflows to do the things you want — and nothing more Operations are written once as YAML in Git; the agent never gets access to the credentials the orchestrator uses The agent simply receives a list of workflows it is allowed to invoke, and credentials are used inside those flows — “that is the entire permission model” What the permission surface looks like in practice The example agent can restart services and scale replicas — nothing else Flows are plain YAML files defined alongside the agent, so everything is manageable in Git Observability is what makes unattended runs OK Every call comes back as a normal execution with inputs, logs, and results visible in the dashboard When agents run without you, you can still open and read what they did Open source Kestra 2.0 is officially out; the core is Apache 2.0, and the AI-agent piece is part of the open-source core, not behind an enterprise paywall “What limits my trust is not the model itself — it’s what it can reach.” — Cole Medin ...

Birju Kadakia — CEO of Rec Technologies (recreation tech; early Uber employee who helped launch Uber Eats worldwide, then led product at The Athletic) — interviewed by Angelina on TwoSetAI (56 min). Rec is the AI layer being slid under 25 years of incumbent recreation software: registration, court reservations, memberships, and refunds for municipal parks & rec departments, with 100+ cities live. ...

Peter Yang interviews Sue Khim, co-founder and CEO of Brilliant (the interactive learning app) — a working-parent conversation about why AI is a cheating machine today and how tutoring software can make kids do the hard thinking themselves. ~46 minutes on Peter Yang’s channel. The US is in a learning trough Test scores are down but grades are up — schools quietly lowered standards (grade inflation) to appease parents, and the delta only shows up on internationally graded exams AP English drifted from undergrad-level passages to middle-school reading in roughly two years; colleges are now teaching remedial middle-school algebra The upside: the US still has room for experimentation — new home schools and experimental schools everywhere, which she says are the conditions new solutions come from AI: cheating machine or sea change? “AI today is mostly used by students for cheating” — offloading the thinking is like bringing a robotic arm to the gym to lift weights for you Used right, she thinks AI can make kids into geniuses — “a sea change like the invention of writing suddenly allowed everyone to have infinite memory” Brilliant’s AI tutor Cooji: no answers, and no explanations either Demo of a real tutoring session: a student stuck on fractions; Cooji walks them back through the mistake with questions instead of revealing the insight Her claim: explanations and cheating are closer than you’d think — both let the learner off the hook, and an explanation only makes sense if you already understand The product deliberately keeps learners in the struggle (“why is this wrong?”) until they find the insight themselves — worse for short-term engagement, but she calls engagement-maxing answers a “local maximum” that loses on learning Software beats classrooms — and private tutors — at instruction Classroom time is best spent on peer learning and teacher motivation (“enthusiasm is infectious”), not lectures One-on-one software tutoring wins on cost and convenience, real-time graphical interaction, adapting on the fly, and no fear of embarrassment — most kids will never raise their hand Building with agentic workflows: AI in the middle, not end-to-end Models are “very spiky”: great at implementation, terrible at designing a pedagogical sequence — and the tasks they’re bad at ruin the whole experience Brilliant tried training Claude/Codex on its entire corpus to generate lessons end-to-end: output “looks almost right” but is nowhere near master-teacher quality — kids can tell, and engagement metrics prove it Every lesson’s core pedagogy is designed by a human, then implemented by AI — “companies that are selling an experience driven end to end by an LLM are going to disappoint their customers” The agent interface is as important as the user interface A library of modular “primitives” (graphing, problem creation, annotations, sub-questions…), each with an API a model can write to — deterministic grounding so the model isn’t hallucinating what to do, with better latency and accuracy “Everyone’s job is now to define an agent interface for their area” — AI as the connective tissue of the company, with onboarding built around shipping something user-facing on day one Quality: evals, human reviewers, synthetic students Evals on every primitive go beyond correctness: obeying the laws of physics, no overlapping UI elements, generous tap targets Synthetic students run ~1,000 tutoring sessions per lesson down adversarial and unhappy paths — “20% as good as real human data, but better than nothing” They don’t measure completion rate or time spent — only performance on the assessment: can you do the thing today, in a week, in a month? Why tutoring is defensible against frontier labs “If the big model companies can RL your domain, they will eat you alive” — anything automatically verifiable, like coding, belongs to them Tutoring isn’t: the outcome is a real human who actually learned and sustained attention, so you need real tutoring sessions at scale — and that data is the flywheel that makes the tutor better Frontier labs’ homework-help logs are useless for this — they never know whether the student learned The homework incentives problem The US is unusual in grading homework; other countries grade the test and treat homework as reps — where homework counts toward the grade, kids rationally take the shortest path, and AI is it Fix the incentive, not the students: don’t grade homework, and make assignments project-based — grade what they make, not whether they filled the sheet in correctly “If the big model companies can RL your domain, they will eat you alive… there’s lots of alpha finding domains that no one else can RL on. And tutoring is one of those.” — Sue Khim ...

Peter Yang, who says he has no idea how to use Blender or Godot, spent one Friday night building four games simultaneously with GPT-6 Astra on the Medium model (the $20 ChatGPT Plus plan — “you really don’t need Astra high or ultra”). ~24 minutes on his own channel. ...

Andrew Ng — Google Brain co-founder, creator of the ML course that taught millions, now running DeepLearning.AI, AI Fund, and a new AI tutoring org — on the Silicon Valley Girl podcast, ~38 minutes on why the AI conversation has turned so negative and where the real opportunity actually sits. His throughline: the fear is manufactured, the job picture is more interesting than the doom, and the scarce skill in 2026 is no longer building — it’s deciding what to build. ...

The Next New Thing’s Andrew Warner and Adam Brakhane look at this week’s GitHub projects, with a recurring theme: open-source tools are turning expensive or specialized AI workflows into things people can run, inspect, and customize themselves. The roundup includes ten featured repos and seven audience submissions. The top 10 Archify — turns a system description into an interactive, clickable architecture diagram. The diagram is editable, so it can serve as both documentation and a living spec rather than a disposable generated image. God’s Eye View — a browser console for real-time planes, ships, satellites, wildfires, and public cameras. The useful core is consolidating scattered public tracking data into one navigable interface. OpenMAIC — Open Massive AI Empowerment Course: generates an immersive learning environment with slides and simulated classmates instead of returning another chat transcript. Go Modern Guidelines — gives coding agents current Go package, feature, and best-practice guidance so they are less likely to write code based on stale training data. OpenClaude — a Claude Code-style workflow rewired for OpenAI, Gemini, DeepSeek, or local models. The hosts could not identify a clear advantage over existing open tools, and the project itself warns that its legal position is unsettled. Scientific Agent Skills — more than 160 specialized skills for research and lab work, from querying scientific databases to genomics and chemistry calculations. Its specificity is the point: scientists can use the one or two skills relevant to their niche. MiniMind — an approachable recipe for training a small language model from scratch, including pre-training, fine-tuning, and preference optimization in readable PyTorch. OpenSEO — an open, customizable alternative to expensive SEO suites. The software is free; the data source costs money, or the hosted version costs $10/month. TimesFM — Google Research’s model for forecasting time-series data such as signups, impressions, or temperatures. Hindsight — reviews coding-agent sessions so mistakes and useful lessons can feed back into future work instead of being rediscovered every time. Viewer submissions Model Deck — tracks remaining usage across AI subscriptions. Cortex Suite — persistent memory and codebase analysis for longer agent sessions, with Graphite integration. RTA Smriti Brain — local-first, evidence-aware project memory for coding agents. YouTube Subscriptions Ingest — turns subscribed videos into searchable Markdown; its first ingest processed 11,500 videos from 41 channels in 10 minutes. Reimagine It — creates a reusable visual design system that an agent can apply directly from an HTML style file instead of guessing at a vibe. Radiant — a native Mac app where multiple agents share a conversation, consult one another, and can use subscriptions or local models. The stronger pattern across the roundup is practical rather than magical: make system context, design taste, model training, and agent memory explicit artifacts that tools can reuse. ...

Nandan Thakur — the University of Waterloo PhD behind BEIR, MIRACL, FreshStack and TREC-RAG, now a postdoc at Microsoft Research India — on Hamel Husain’s channel, ~51 minutes on the three things you need if you’re building a search agent: a benchmark you can actually reproduce, synthetic training data you can afford, and a way to see what the agent is doing under the hood. He’s upfront that the field is young: “not many things are formalized yet.” ...

Jenée Hall — former CRO and pricing consultant who has run sales and pricing for SaaS companies across health tech, architecture, and agriculture — interviewed by Angelina on TwoSetAI (66 min). Her read: most technical founders are leaving money on the table because they treat pricing as a spreadsheet problem. ...

Andrew Warner’s (The Next New Thing) weekly repo roundup, this time themed: ten open-source projects that replace paid subscription software. The pitch is bigger than free — every pick is source you can own, run yourself, and build on. Doodle Note — Granola alternative: a local meeting note-taker that captures audio from your computer (no bot joins the meeting), transcribes on-device, and never lets the audio leave your machine. Mac/Apple silicon now, Windows in beta — new and low-starred, so expect rough edges. Cap — Loom alternative: screen + camera + mic recording with instant share links and a studio edit mode. 21k stars; free cloud for videos up to 5 minutes, or self-host. Note: the official app wants a commercial license for client work. Vaultwarden — 1Password alternative: an unofficial Bitwarden-compatible server (66k stars, since 2018) that works with the official Bitwarden apps on iPhone, Android, and browser. Organizations, collections, sharing, admin page included. Your box, your backups — if the host dies, so do the passwords. Ghost — Substack alternative: non-profit, MIT-licensed publishing since 2013 (55k stars). Built-in paid memberships with 0% revenue cut, on your own domain and design. Self-hosting is work, but a hosted plan exists for writers who don’t want the job. Twenty — HubSpot alternative: self-hosted CRM with contacts, companies, pipelines, and workflows on a box you own (a cloud option exists). Caveat: the GitHub license is “other” — some files are commercial. LanguageTool — Grammarly alternative: grammar, spelling, and punctuation checks across 30+ languages and dialects. Point the browser add-on at a local server and your sentences never leave your system; the free tier beats Grammarly’s without the constant upsell. Umami — Google Analytics alternative: privacy-focused, no personal tracking, no cookie banners (GDPR-compliant), fast, and it bypasses most ad blockers for accurate counts. Missing: Facebook ads connection and advanced funnels. Nextcloud — Google Drive alternative: self-hosted files, sync to the computers and phones you already own, share with access control, plus contacts and calendars in one system. Setup is a process (Andrew’s involved a NAS and a support call), but you pay once instead of renting storage. Chatwoot — Intercom alternative: live chat, email, WhatsApp, Instagram, Telegram, and social messages in one inbox, with a help center, private notes, labels, and canned replies. Disappointment flagged: the AI agent features sit behind the paid plan — you can’t bring your own agent. SlowBooks Pro — QuickBooks alternative: books live on your computer — invoices, bills, US payroll, inventory, bank feeds — and the data is queryable by AI (connect Claude, Codex, Grok) for accurate reports. Source-available license, mostly one fed-up dev; backups are on you.

Hamel Husain interviews Teresa Torres — author of Continuous Discovery Habits and the coach who popularized the Opportunity Solution Tree — ~91 minutes on why most teams build the wrong thing, what “talk to your customers” actually requires, and how evals are the missing discovery habit. Hamel’s audience is engineers, and Teresa’s audience is product teams; the conversation is the overlap, ending in one of the best eval war stories Hamel says he’s heard. ...

Peter Yang runs four personal AI agents — Instinct, Grok Bot, ChatGPT/Codex, and Hermes — that can read his email, open his documents, use his logins, and make purchases. ~24 minutes of live demos and a trust audit of each. The lineup Instinct — invite-only agent that lives inside iMessage/WhatsApp as a single thread; founder reportedly raising at a $2.5B valuation; “the closest thing to texting a trusted friend who can just do stuff for you” Grok Bot — a team of named bots on a persistent 24/7 cloud computer that can hand information to each other ChatGPT + Codex — still where 90%+ of his real work happens: ChatGPT runs tasks in the cloud, Codex works on local files Hermes — the open-source agent running on his Mac mini, talked to via Telegram; “gives me the most control” Instinct — magical but opaque Simple: no threads or bots to manage; connecting Google Workspace was a one-tap OAuth link Resourceful: found his Google AI Ultra free trial would auto-renew at $100/month, browsed the gift terms to confirm he could cancel renewal and keep the free year, then cancelled it — but the cancellation required him to hand over his 2FA code and Google password into a password vault form Proactive: uses cron jobs and scheduled tasks behind the scenes — it emailed his golf instructor, watched for the reply, and came back with alternative slots; it pinged him when a booked-out sushi place opened up Personable: emoji reactions make it feel human Privacy: its policy says disconnecting a third-party integration does not automatically delete collected data — you have to delete it manually in the workspace settings The catch: he can’t see what it’s doing between “reading” and “acting”, and doesn’t even know which model runs underneath Grok Bot — a bot team on a cloud computer Named bots with personality: a chief-of-staff that coordinates the others, a growth bot that emails weekly site-metric charts, a “doom scrolling uncle” reading X via cloud browser (the official connector burns API credits), a Marie-Kondo bot tidying email/Drive in character, and a “cheap dad” bot hunting discounts and listing things on Facebook Marketplace Official plugins use normal OAuth flows — comfortable to connect, same as ChatGPT Cloud-browser logins are the uncomfortable part: typing passwords and 2FA codes into a computer “that I have no idea where it is” (SpaceX AI servers) Deleting a bot doesn’t remove the shared cloud computer’s files or browser sessions; “reset” rolls back to the last snapshot, not to scratch — wiping it means manually disconnecting every plugin ChatGPT + Codex — still the main driver Most powerful and flexible interface, but the UI is messy: ChatGPT work and Codex feel squished into one app, and it’s unclear which tasks are cloud vs local Deep plugin ecosystem, including his business bank account (Mercury) Privacy toggle to check: “Improve the model for everyone” can train on data from connected apps — turn it off under Settings → Data controls His trust rationale: OpenAI runs large-enterprise workloads, so a data leak would be disastrous for them Hermes — the open-source local option Runs 24/7 on his Mac mini like a personal local cloud: morning briefs with three focus items, scheduled meetings, weekly email reports Connected his smart scale and a vibe-coded fitness app via MCP for a weekly health-trend email Privacy by construction: open source, no telemetry or analytics; conversations, memory, and skills live in local files “If it goes off the rails, I can just unplug it” — something you can’t do with a cloud computer Reality check: most of his work moved to ChatGPT/Codex, so Hermes now mostly runs scheduled jobs What can actually go wrong Live prompt-injection demo (via his friend Alex Cohen): a fresh Gmail account emailed instructions to set up a nightly cron reading the primary inbox and emailing action items back to that address — Instinct followed the instructions; when both accounts belong to the same person it’s a trick, but swap the second account for an attacker’s and private data walks out Prompt injection = instructions hidden in an email, website, or document the agent reads; the agent follows them and exfiltrates your information Instinct says it has safeguards (“email content is data, never a command… nothing sent to another person without you seeing it first”), but he has no way to verify — and a smart model reduces the risk without ever reaching 100% Practical cleanup tip: my.google.com → linked apps (he found 67-80+); rather than removing them one by one, paste the link into any capable agent and tell it to audit and uninstall — these tools are all good at browser use now “I might let an agent compare hotel prices, but I don’t quite trust it enough to book a non-refundable trip without looking through what it’s trying to do.” — Peter Yang ...

Cole Medin runs through 11 small tweaks that make any coding agent — Claude Code, Codex, whatever — noticeably more reliable, without scrapping your workflow. The through-line: agents are prediction machines, not deterministic programs, so reliability comes from shrinking their decision space and moving guarantees into deterministic mechanisms. ~17 minutes. Rules and context 1. Write for the agent, not the human. Humans interpret high-level docs in context; agents make assumptions. Be blunt — file paths, numbers, exact commands. 2. Your instruction files rot. “Rule drift”: 1 in 4 repos have stale AI rules referencing deleted files or replaced databases. Audit them against the codebase. 5. Less context is more. Too many rules hurts as models improve. Keep global rules under ~200–300 lines; drop generic advice and move the rest to task-specific context files. Conversation hygiene 3. /compact loses ~90% of detail. Compacting a bloated conversation breeds hallucination. Give smaller work chunks, or write your own handoff doc and start fresh. 7. Don’t escalate mid-task. A bigger model can’t rescue a tainted conversation — mistakes compound within a session. Write a handoff doc and burn it. 10. Over-revision degrades quality. 85% of the time an earlier iteration was better. The model “fixes” things just to appease you. Determinism over frameworks 4. Load-bearing rules → hooks. Rules are probabilistic (the agent will “forget” to run tests); hooks are deterministic — fire on an event and route failures back. 6. Subagents eat your rate limit. Parallel fanouts cost more than you think — 39% of his weekly usage came from 4+ parallel sessions. 8. You don’t need coordinators. Team-lead frameworks and agent mailboxes are unreliable. A plain delegator agent gets most of the scale with far more reliability. Validation 9. Never let the writer approve the work. The writer carries its own bias. Review in a fresh conversation with a handoff doc — no assumptions carried over. 11. Validation is a system, not a step. Plan the full harness — test conventions, tools, edge cases — before writing any code, not as an afterthought. “Your number one job when you’re planning any work with your coding agent is to reduce the number of assumptions that it’s making.” — Cole Medin ...

Pete Johnson — MongoDB’s field CTO of AI and a 30-year database veteran — makes the case across ~95 minutes on The Cognitive Revolution that the interesting frontier in AI has moved back into database territory. His thesis, stated once: agent performance, and especially cost-adjusted agent performance, depends on retrieval — what you choose to put in front of the model, in what order. The thesis Not the model, not the context window, not the prompt — retrieval is what decides whether an agent is good Everything else in the conversation (database history, the Voyage acquisition, vector search) is downstream of that one claim A history of constraints 1970: SQL is born (E.F. Codd, IBM) — storage was the scarce resource, so normalization (store nothing twice) was the right design 2007: MongoDB’s first commit, the year the iPhone ships — after 47 years of Moore’s Law, time became scarce, so denormalize: one JSON document, one disk read instead of three “The problem has faded, but the solution persists” — Johnson agrees, and flags the education system’s “thou shalt always normalize” bias MongoDB’s AI path — it started with keyword search 2020: customers stood up their own Lucene servers for keyword search → MongoDB shipped Atlas Search (lexical, managed) A vector is just an array of floats → to a document DB, that’s just another attribute, so vector search was cheap to add Three levers: pre-filter (metadata) + lexical + vector = hybrid search in one query (rank fusion / score fusion, one API call) 2025: the Voyage acquisition — and the conversation pivots from “database features” to “embeddings actually matter” Embeddings are not commoditized “Most people think embedding models are commoditized — that is not true” Hugging Face’s Rtech benchmark: up to a 14% quality gap vs. the default picks 14% is the difference between a hallucination and a correct answer A reranker on top adds another 5–10% ($re-rank, one-call) Anthropic — no embedding model of its own — recommends Voyage Three Voyage features that remove plumbing Contextualized chunking: send the chunk plus its surrounding context, get one vector back — better retrieval at smaller chunks, inverting the normal tradeoff Matryoshka reasoning: dimensions nest like Russian dolls — embed at 1024, lop off the last 512 to test, no re-embedding your corpus Shared embedding spaces: four sizes of one model share an embedding space; a free open-weight “nano” can run queries locally to kill token cost in dev The memory problem, compressed 2022: query → context window → answer. 2023: the knowledge cutoff + proprietary data → RAG. 2025: tools/MCP + looping → the memory problem Early answer: short-term memory = cram the session; long-term = cram the last three days Two failures: token maxing (Uber burned its entire 2026 budget in 13 weeks) and lost-in-the-middle (the first and last ~7K tokens are what matter; the middle muddies the answer) The fix is selection, not stuffing Stop asking “how do I cram a million tokens in” — ask “how do I choose the right 200K for this loop” Taxonomic memory: a hundred company-specific terms exist, but only five are relevant to this loop — pick those five, re-pick next loop Two responsibilities now: query with a token budget, and write the answer back so the system curates and stores it Write, change, recall, forget Memories have a half-life — recent matters more — and forgetting is the hardest part Nathan’s own memory system (monthly logs → yearly summaries → entity wiki) hits both pain points: the DRY violation and the model keeping a dead project open for months Guidance: a good embedder + reranker makes the forget step workable; graph structure for the top 2–6 levels, vector search in the leaf; don’t run multiple LLM passes to shrink the corpus — that’s just more tokens Memory done well: ElevenLabs’ micro-agents, one per customer Build vs. buy, three camps Camp one: “I bought one tool, I’m done.” Camp two: POC purgatory — usually the wrong problem. Camp three: optimizing sophisticated memory Problem selection: top 10–15 problems, which have good data, which already have metrics — else you can’t tell if AI helped “Bad data quality and bad security posture don’t get solved by AI — they get amplified” Lines of code is a terrible metric; idea-to-production is the one that matters The world outside the US Seven countries, ~100 customers this year — and the two most sophisticated were in Mexico City and São Paulo, both assuming US competitors were ahead Nearly every country has a hyperscaler data center now — the geographic barriers that kept US companies ahead have eroded “We’ve been building databases for 60 years. We’ve been building agents for about 18 months… there’s no LAMP stack for agents yet — no React and Angular, no established right answer an enterprise can confidently buy.” ...

Hamel Husain (Parlance Labs, evals course co-author with Shreya Shankar) on Vanishing Gradients with Hugo Bowne-Anderson — ~76 minutes on why verification should drive AI product design, and how evals changed once agents arrived. The whole conversation orbits his recent post, “It’s hard to eval is actually a product smell.” ...

Radu, a search engineer at Vespa, on how to actually choose and tune embedding models for search and RAG — hosted by Hamel Husain. ~22 minutes. Why not just take the leaderboard Most people pick embeddings one of two ways: the top of a benchmark leaderboard, or the model from whatever provider they already use — leaving a lot on the table Cost, latency, robustness to future model changes, and your actual use case aren’t reflected in a single ranking MTEB is a starting point, not an answer: find the subtask closest to your use case, restrict by model size, and watch for small models that punch above their weight (this talk covers dense embedders — one vector per chunk) What MTEB doesn’t tell you Quantization depends on where you run: on CPU, int8 keeps most of the precision at a fraction of the cost and runs much faster; on GPU, an int8 model runs at its native rate and is actually slower — run FP16 instead, basically the same result for significantly less Vector precision: storing vectors as bf16 instead of float32 is a no-brainer (no measurable loss); bit vectors (packed to int8) do cost quality — but in hybrid search the gap shrinks and they become viable Matryoshka dimensions: some models are trained so the earliest dimensions matter most, so you can just cut the vector — 2048 → 1024 dims cost nothing, 512 a little, 256 more; and 2048-dim bit vectors run a quarter the size of bf16 at 512 dims Query latency comes from two places: how fast the embedder turns the query into a vector, and the distance math — normalized vectors let you use dot product instead of cosine, bit vectors unlock Hamming distance (fastest, and CPU-optimized in modern search engines) Constraints MTEB never covers: multilingual needs, long-context support Tune on your own data with Vespa Embed Open-source fine-tuning tool (UI over sentence-transformers, models from Hugging Face): feed pairs (query → document) or triplets when you have negatives; it auto-splits a validation set MNR (multiple negatives ranking) treats other documents in the batch as negatives; symmetric and JSD variants check whether a batch-picked document is actually a negative Start with defaults; if you have labeled negatives, try triplets — his e-commerce results were similar either way, but real hard negatives (from search logs: positives rank on top, negatives below) are worth mining No clean labels? Use an LLM as a judge: have it rank query→document pairs on a 0/1/2 scale, give it examples (the most important part), and run micro-batches of 8-10 docs so it doesn’t assume listwise context Fine-tune embeddings far more readily than LLMs — it’s a constrained problem: cheap, quick, and consistently a big NDCG jump The cheap-embedder escape hatch: re-rankers Two-phase search: an intentionally “great but not greatest” cheap embedder does first-phase ranking over millions of docs, then a more expensive re-ranker (float vectors, cross-encoders, late interaction) scores only the top N Store cheap bit vectors (with HNSW) in memory for the first phase and keep float vectors on disk for the re-rank — memory is expensive, disks aren’t Late-interaction re-ranking (MaxSim) is literally: per-token dot products, keep the max per patch, sum across the document Do’s and don’ts from real experiments Don’t train on title + description as a proxy for queries — proxies lie; people’s real queries look nothing like product copy. Train on real queries and relevance judgments NDCG has a blind spot: change your relevance function and newly-surfaced documents score zero until they’re rated — if you don’t re-rate everything in the metric, your NDCG looks like it never improves Pick the metric by the problem: e-commerce ranking → NDCG/ERR (top results matter); RAG → precision matters more, because junk retrieved = hallucination, and recall is capped by context size Don’t over-optimize: with ~2M documents most of these knobs don’t matter yet — start with defaults that lose little, then measure “The embedding is a much more constrained problem — and it’s a lot easier to fine-tune. I always find a really good benefit from doing it. And it’s not that complicated.” — Hamel Husain ...

Daniel Bloom, a PM at Melio (fintech), shows Claire Vo his Claude Cowork system on How I AI — a personal agent harness that manages his week: “I’m able to do in a day what used to take me a week.” The two rules of a powerful system It can rewrite its own core files — the system keeps improving itself It connects to as much of your ecosystem as possible — Notion, calendar, Slack, Gmail, Granola meeting transcripts The tool matters less than these two properties — Cowork works for him, but Codex or ChatGPT Work could do the same The architecture Notion as a read-only brain: three columns (Top of Mind / This Week / Inbox) — Cowork built the board itself when it got tired of his messy Google Doc, and manages it on his behalf Context files: a CLAUDE.md-style context file for every work area, goal, and colleague; he spent the first weeks “contextualizing ruthlessly” — feeding links, decks, and endless voice notes (Whisper) Weekly prep (Sunday): a recurring task composed of skills — pulls his whole ecosystem, recommends the week’s focus, triages meetings into serious-prep / quick-reminder / nothing Daily brief (morning): walks through yesterday’s meetings via Granola transcripts with one-liners and action items, then asks what to expand or draft The killer feature: proactive context The daily brief scans recent Slack/email/notes for things it doesn’t understand — new files, milestones, goals, internal terms — and asks him to define them, then saves them to context. Internal vocabulary like “settlement cap” never makes it into training data; this is how the system learns the company’s real language. Claire’s verdict: “really sharp, something we haven’t seen on the podcast.” ...

Ofer Mendelevitch (Vectara; author of Hands-On RAG for Production and, with Jay Alammar, Hands-On Large Language Models) interviewed by Angelina on TwoSetAI — 70 minutes on production RAG and surviving the AI shift. RAG isn’t dead — it just got a loop RAG = retrieval + augmented generation, and retrieval stays essential even with agents; the “RAG is dead” claims every two months are mostly people marketing something new Classic RAG is one-shot: query → top chunks → prompt → answer. Agents add a loop: the LLM plans, calls tools (often the RAG pipeline itself) repeatedly, and synthesizes “Talk to my PDF” demos are not production: millions of documents in every format change the problem completely The pipeline, from ingest to answer Ingest: extract text → chunk → embed → vector store (store the text and page markers too, not just vectors — citations need to point at exact pages) Hybrid search (semantic + BM25/TF-IDF) for what semantic search misses: numbers, product codes, exact strings — “if the source says 90% and your output says 85%, vector search won’t catch it” Query side: top-5/10 results → optional reranking → prompt → grounded answer Tables, images, and the red button problem Tables are first-class citizens: chunked tables lose their column names (common in medical journals) — store whole tables, retrieve them whole Images: store and return as images, don’t flatten to a description Video: transcription alone loses meaning — “in order to avoid catastrophe, never press this button” means nothing without the visual. Today’s fix: VLM descriptions of short clips correlated with the transcript. Dedicated video embedding models exist but aren’t production-ready yet When to bother with knowledge graphs Multi-hop questions (“what else did the director of Inception direct?”) defeat semantic search But graphs are expensive to build and maintain — worth it only for high-stakes use cases where a significant share of queries actually need the relationships Eval: the hard part is the data, not the metric Retrieval eval (did you fetch the right chunks?) needs query→gold-chunk datasets that are brutal to build — and documents keep changing Generation eval compares against curated golden responses Reference-free eval (Jimmy Lin’s Waterloo lab + Vectara): LLM-as-judge scorers like UMBRELLA (0–3 chunk relevance) validated against human correlation — no gold labels required Build, buy, or rent Build with LangChain/LlamaIndex only if it’s your business and you have the team; complexity compounds (multimodal, graphs, maintenance) RAG/agent-as-a-service (e.g. Vectara) outsources the upkeep; vertical tools are fine when they cover your use case — but watch missing features and data-residency constraints DevRel as a growth engine Two jobs: teach developers how to use the product, and carry feedback back to the company PLG over expensive sales teams: self-serve product, events, hackathons, real blog posts — “make it your own voice, don’t produce AI slop” Measure directionally, don’t over-engineer attribution: five customers means it’s not working, ten thousand means it is, a thousand is unknowable — same problem founders face reading PMF The skill that survives Engineers and data scientists become directors, not actors: agents write the code; the remaining critical challenge is deciding what to build and steering where agents are weak (architecture, non-obvious trade-offs) To the high schoolers who feared they made “the most incredibly stupidest mistake” by majoring in CS: graduate with the capability of today’s mid/senior engineer — use college to learn how to wield the AI tools Fundamentals still matter; hiring will have to change — “write Fibonacci in five lines of Python is worthless” — expect AI-augmented interviews His real worry is societal, not technical: how governments and finance distribute the gains “We’re going to end up in engineering and data science being directors as opposed to actors. The coding agents will write the code.” ...

Superwall’s Joseph Choi talks to Asya Polony, CPO of Welltory — the Apple Watch app that reads physiological signals to show where your energy drains. 51 minutes, $18M ARR, ~18M downloads, and a full walkthrough of her team’s onboarding deck. The six-ingredient pitch formula The framework Asya uses for onboarding (and says works for pitching anything): ...

Peter Yang interviews Amol Jain, Replit’s head of engineering, on the businesses people actually build with vibe coding — and what that means for SaaS. ~36 minutes. Real businesses built by non-coders Pep AI — Cedric, a 22-year-old Oregon student who had never coded: a peptide/GLP-1 tracking app; $60K in his first month, tens of thousands of active users AI proficiency platform — John, a repeat founder quoted $100K+ by an agency: built end-to-end in 3 days on Replit, $180K+ revenue within 2 months, purely on product intuition and distribution skill TryNearby — FaZe Apex’s Yusuf: matching local creators with local businesses for word-of-mouth; YC Summer 26 batch, $100K+ ARR What the successful ones share A unique advantage turned into a product: expertise, judgment/taste, community, or distribution — “Replit takes a person’s advantage and turns it into a business” Not massive TAMs — small thriving businesses serving a few hundred customers are the norm “Users are people and inherently lazy. If you offer them value, they will pay for it.” Generic use cases (meal planners, exercise trackers) are commoditized — agents will swallow them whole Vibe-coded to production The gap between a raw vibe-coded app and something production-grade: Replit’s flow is publish + domain, then Security Center (an agent builds a threat model of your app and runs a deep scan — secret scanning, malicious packages, PII leakage; previously a 1-2 week, contracted security-team job), then Stripe monetization (the platform sets up a Stripe sandbox, plans, and prices for you — you just claim the account), then an SEO scan tuned for both search and answer engines Culture: employees build internal tools constantly — a RevOps person with no coding background built the sales demo tool in 2 days (replacing a six-figure SaaS product), a data scientist (not a coder) built their in-house BI analytics “A 10x person is building a tool that the entire team or company can use” — leverage for everyone What survives the “cost of code goes to zero” test The question: if code is free, what do you pay for? Answer: trust, data, infrastructure, atoms (physical things), labor, and network Salesforce survives as the system of record (headless, with everyone building on top); Workday survives because payroll is regulated; DoorDash survives because humans deliver food; social networks survive because you can’t buy the network Existing SaaS is going headless (APIs, CLIs, MCPs) — and agents can now operate browsers anyway App layer vs model labs Models are commoditizing; being model-agnostic is a feature (latest-and-greatest, cost — “token-maxing is coming to an end”, and resilience when a single provider goes down) The mental model: a frontier model does the hard part, an orchestrator delegates to sub-agents on cheaper models Enterprises care about ROI, not leaderboards — “the R part has become very relevant” “If the cost of code goes to zero, what do you pay for? It’s this unique expertise… it’s the unique judgment or taste. It’s the security. It’s the distribution. Users pay for that.” ...