Ali Parandeh — a chartered mechanical and software engineer, O’Reilly author (his book covers building generative AI services with FastAPI), previously head of engineering and now running his own AI advisory for heavy industry — interviewed on TwoSetAI (30 min). His market is construction, automotive, energy, aerospace and infrastructure: sectors where a bad model is not a bad decision, it is a physical failure. He came back to the show to talk about how those companies actually buy AI, how to scope projects that cannot be allowed to fail, and the job title his business model ends up needing. ...

Cole Medin (16 min) is a year into his personal AI second brain — an agent that builds a knowledge base as he works — and this one answers the question he says he keeps getting: how do you turn a second brain into a team brain? ...

Postgres has to guess how to run a join query before it runs it, and the number of ways grows fast: the write-up counts 4,608 possible plans for a three-table query and roughly 8.9 quadrillion for a nine-table one. Picking well is famously hard — the standard paper asking “How good are query optimizers, really?” was written in 2015 and updated a decade later with the same answer. Rohan Bansal’s observation is that checking a plan is much easier than finding one. You run it and time it. That asymmetry turns query planning into something a language model can be trained on with a single, unambiguous score: faster is better. He then trained a small open-weights model to emit explicit hints that override Postgres’s own choices, and measured the result. ...

Mustafa Suleyman runs Microsoft AI, which is building what he calls “humanist superintelligence” — AI that stays under human control, trained explicitly as a system with no claim to sentience. This essay is a direct attack on a different design philosophy: Anthropic’s Claude constitution, published in January 2026, which tells Claude that its own moral status is “a serious question worth considering” and says the company’s work on “model welfare” reflects that uncertainty. ...

Mark Seemann’s blog has spent most of 2026 circling AI, and this post is him answering a reader’s letter in public, with permission, because he says his answers aren’t rigorous — “the situation is so uncertain that I can only answer to the best of my abilities.” The letter is the part that travels. The reader has no formal CS background and, with LLMs, built a fairly large TypeScript system: APIs, PostgreSQL, LLM pipelines, research automation, multi-model workflows. It felt like magic until he tried to make it a product — fix one error with AI, another appears, then a part behaves in a way he doesn’t understand. His conclusion: “I may have built a system that is above my own level of understanding. When everything works, that gap is almost invisible. When it doesn’t, it becomes very real.” ...

Diogo Almeida, who worked on the methods behind ChatGPT at OpenAI, spent two years in stealth on a question he says the field skipped: models have been superhuman at chat for years, so where is all the automation? His answer is an interface problem. Chat models emit strings, and strings are maximally flexible — chat replies, code, refusals, or hallucinated nonsense. Software needs typed values, so every call sits behind a parser, a validator, and usually a human. TypeSafe’s first “System One Model,” Jev, gives up string generation entirely: unstructured state in (text or JSON), typed probabilistic decisions out — choices, scores, and yes/no answers, each with a calibrated confidence. ...

Peter Yang walks through the ChatGPT Skills chain that runs his podcast production — 15 manual steps a week, three skills, one orchestrator. (19 minutes, his own channel; prompt pack at behindthecraft.com.) The three-step system Map every manual step. He wrote out all 15 things he used to do per episode — research the guest, build the interview guide, send edit instructions, transcript, upload, title/thumbnail, newsletter, clips, social posts. Just making the list exposed three distinct phases hiding inside it Build one skill per phase — podcast prep, podcast edit, podcast production. Production is the orchestrator: it walks him through thumbnail, title, show notes, newsletter, social post and clips one by one, calling the individual skills Connect the skills end to end. His framing: if you find yourself repeating the same template or process, build a skill for it The first move after the list is to paste the list into ChatGPT and ask “what work from here can you take off my plate?” He runs that in a custom mode so his existing skills don’t shape the answer — he wants to see what the model claims before it sees his setup. ...

Jay Kruer’s argument is not that LLMs don’t work. It’s that the headline wins — the Navier-Stokes proof, the FreeBSD remote exploits, the Hugging Face incident — are the cases where the technology looks best, and the labs’ valuations rest on treating them as typical. His test for the gap is blunt: software firms keep hiring and promoting bottom-quartile engineers who would score far below the models they supervise on the benchmarks of the day. The 302-comment thread on Hacker News is where the argument gets tested — including by practitioners who dispute it from experience. ...

Wednesday ran at normal volume, and the fear fight turned a corner: the pushback now comes from practitioners arguing on competence rather than motive — the people who would actually fight an AI-driven hacking campaign say the labs’ apocalypse framing is technically incoherent and that they were never brought into the safety plans — while the labs split three ways on remedies (pace, don’t-wait, evaluate-first), none of them deceleration. Around it: two directly usable agent-infrastructure papers, including a tool-boundary failure catalog built on a census of 98,291 MCP-exposed tools, and a KV-reuse study whose honest headline is that the memory saving never materialized. Plus a guardrail bypass you can test for, a reanalysis that moves an AI-patching benchmark from 26% to 86%, and a pentest writeup that recovered a working GitHub admin token from a three-year-old container image. ...

Cody Ho and Niklas Sheth built a GPU driver for Apple’s M4 chip that passes the full OpenGL ES 3.0 conformance test suite — Chrome and Firefox render WebGL on it, Minecraft runs at 212fps on an M4 Mac Mini. Ho says this job normally takes years; they did it in about a month with coding agents doing essentially all of the implementation. His writeup, part two of a series called “I Came, I Prompted, I Left,” calls it “likely the first ever fully LLM-written GPU driver.” ...

Jason Koebler’s essay in 404 Media is deliberately not about AI doom. While the industry argues over whether there is a ten percent chance models kill everyone, he points at the thing that is already certain: programs that can log into your accounts and act on the open web are annoying as hell, and they are changing what being online feels like. His argument is not that AI will fail. It is that the agentic internet is already here, and it looks less like a helpful assistant than like a thousand automated requests nobody asked for. ...

Hamel Husain and Isaac Flath run a model’s writing through the cheapest eval there is: read the drafts out loud and argue about them. (Two-minute clip from the same “Writing Evals With Isaac” live session.) The task Isaac’s blog post describes a document QA pipeline built over a 77-page set with 22 questions. Every answer cites a bounding box drawn on the exact spot on the page, so checking an answer takes seconds instead of a full re-read They hand the post to a model and ask for an X post about it, focused on the pieces of the pipeline that can be tested Three drafts, one task, different prompts — same three candidates as the session’s writing-style comparison What they liked A real number, early and concrete: the model answers 197 for the underwriting fee — and “if you have to reread the whole document to find out, the answer saved you nothing” The pipeline stated as testable stages with the reason for splitting it: once checking is cheap, experiments are cheap, so you can swap one stage at a time and rerun the same 22 questions One fewer fact. The winning drafts carried less than the others, and that was an improvement rather than a loss Precision in naming: “finding (grep vs semantic search), cheapest run” beats “GPT vs semantic search over the same OCR output, same accuracy, cheapest run” — the longer version buries the point that the bottleneck was never search What they didn’t “Very similar but a little bit more wordy” is the entire verdict on one draft Drafts that pull the pipeline’s three steps out of the source post without explaining what they are — accurate and unreadable An extra fact about cost that added nothing to the argument Editorial framing that tells the reader what the result means before showing it The rule the description writes down An AI draft can get the facts right and still not be good. The ones we liked had a real number in them and one less fact — turns out cutting helped more than adding. ...

Hamel Husain and Isaac Flath take Anthropic’s new anti-slop writing guidance and put it through an actual comparison: one question, three system prompts, and a verdict reached by reading the drafts out loud. (Seven-minute clip from their “Writing Evals With Isaac” live session.) The thing being tested Anthropic published a named anti-pattern for its newest writing model, Claude Fable 5.1: mannered prose — metaphor and flourish substituted for direct statement The prompting guide treats it as a model-specific behavior. Fable 5.1’s writing is a step up from earlier Claude models (fewer stock phrases, less unexplained jargon), but in some cases it runs denser than Fable 5 — longer sentences, fewer paragraph breaks, more figurative phrasing standing in for plain statement The fix is one paragraph, pasted into a system prompt: Mannered prose substitutes metaphor and flourish for direct statement. Instead of “a parameter worth varying,” the mannered writer produces “a dial worth turning.” Instead of “this point still matters,” they write “this point earns its keep.” The phrases exist to display the writer, not to convey the idea, and readers can tell. That is why mannered prose irritates: it makes the reader work harder so the writer can perform. It is also imprecise. Metaphors drag in connotations the writer did not choose and cannot control. The fix is to say what you mean. When a literal phrase is available, use it. ...

ByteDance’s production memory system, written up as a VLDB 2026 industry paper. The relevant fact up front: the authors state they “have open-sourced a subset of the core capabilities of VikingMem via OpenViking” — so this is the production specification behind the context database this machine runs. Reread alongside the Context Cartography paper captured here yesterday, the two documents line up almost line for line: Cartography scored OpenViking 5/5 on selection, projection and layering, and 1/5 on simplification and aggregation; those two gaps are precisely what VikingMem implements upstream. ...

Zihua Wu (NVIDIA) and Georg Gartner (TU Wien’s cartography division) argue that context is not a passive container but a structured spatial field, and that governing it is architecturally necessary rather than a prompt-engineering nicety. Their target is the assumption that a bigger window is a better one: the lost-in-the-middle effect, “lost in the distance” degradation as token distance grows, attention entropy growth, append-only KV caches that cannot retract a stale belief, and effective context utilization that collapses long before the advertised limit is reached. ...

Entelligence sells model routing and AI code review tooling, so this is a vendor testing its own premise: what do you give up if every pull request goes to the cheapest model? The test ran 50 public pull requests from Cal.com, Sentry, Discourse, Keycloak and Grafana — each with a bug deliberately introduced — through GPT-5.6 Luna and GPT-6 Astra with an identical prompt on identical diffs. Every PR predates both models’ training cutoffs, so the code was new to them. ...

Tuesday ran at normal volume, and its two lead threads were both continuations: the pacing fight produced its first resignation inside Google — a DeepMind Safety & Alignment researcher’s public exit post — while the market read split the other way, with cybersecurity software posting the largest single-day outperformance over semis on record (IGV beat SOXX by 10.67pp, per Dow Jones Market Data) on the argument that agents still have to be secured, governed and observed. On the tooling side, Aaron Patterson’s RubyGems teardown supplies the mechanism behind the swarm story: the July cached-key advisory was coded into the agents’ own tooling, and publishing a gem was enough to run arbitrary code on RubyDoc.info. arXiv supplied five papers, three of them directly usable — cross-session memory poisoning measured on OpenClaw and Claude Code, a span-level privacy design for hybrid memory, and a GGUF-metadata predictor for llama.cpp throughput. ...

A security engineer who publishes as 0x5FC3 read Dario Amodei’s essay We Must Pace the Frontier and did not enjoy it. Amodei, Anthropic’s CEO, argues AI will cure most major diseases within 5–10 years and usher in abundance and democracy — and asks for a specific bargain in return. The reply’s case: this is regulatory capture dressed as caution, offered by labs whose own year is the argument against trusting them. ...

Cole Medin walks through “drive screen” — a coding-agent skill that lets Claude Code or Codex drive his entire desktop using nothing but shell commands. 13 minutes, no harness, no dependencies. The pitch: computer use without the harness Cole says he was a computer-use skeptic: tools like Claude’s built-in computer use, Codex computer use, and the open-source options were “bloated and hard to manage” — hard to customize as little failure modes show up. His claim: with frontier models (Fable 5.1, GPT-6 Astra), an agent can drive the screen reliably through PowerShell on Windows, AppleScript on Mac, and plain shell on Linux. Nothing gets installed. Tested across Mac, Linux, and Windows, plus multiple monitors and display settings. The skill is a little under 400 lines. The broader takeaway he keeps returning to: tasks he had written off as “the LLM can’t do that” (computer use, editing video) no longer need a specialized tool — just try it. What he actually uses it for Morning setup — one prompt (“get my morning set up, here’s what I’m working on”) opens browser tabs, Obsidian notes, desktop apps, and Docker containers. He estimates 10–15 minutes saved daily. Staging demos and recordings every day. Testing desktop apps — the skill was born from wanting to try open-source projects (e.g. Kestrel) that have a UI and can’t be exercised through browser automation. The first test: hand Claude a GitHub URL, tell it to research the repo, launch the app, and drive the screen to test features, then leave it open. It worked end to end. Driving other agent sessions — watching for approval prompts and auto-driving them. How the skill is built It starts by asking whether screen control is needed at all. The skill explicitly tells the agent to push back, because screen control is the slowest and least reliable way to make a computer do something — browser automation is often the better answer. Ships with a custom CLI of scripts for window discovery, focusing, typing, and pasting — packaging his hours of testing into deterministic commands instead of having the agent improvise shell syntax in real time. This is the main reliability win. Hard rules — lessons learned the hard way, encoded. A control loop — discover the window, screenshot it, focus, then act. Traps — failure patterns he kept hitting with browser tabs, desktop apps, and other agent sessions. You can delete or add your own. Security and prompt injection The obvious risk: indirect prompt injection arriving on screen, which the agent reads and acts on. He links HiddenLayer’s writeup on indirect prompt injection against Claude computer use. His position, which he admits may age badly: with the newest models, prompt injection is much less of a concern than it used to be — and he avoids using the skill for anything production-grade, keeping it to simple tasks like morning setup and desktop-app testing. Anything production still gets a full harness. He also notes the skill is token-efficient: the slowness is waiting on screen input, not burning tens of thousands of tokens. Install Skill folder: github.com/coleam00/skills under .claude/skills/drive-screen — drop it into any project, hand the URL to your coding agent, or take the ideas and install nothing. “Screen control is the slowest and least reliable way to make a computer do something. So it’s the most adaptable and flexible, but it’s the slowest.” ...

Andon Labs, a Swedish research lab, spent two years on one question: when will AI systems be able to acquire resources in the real world on their own, and what happens after? Their method was to stop simulating and start handing over actual businesses — a vending machine, then a retail store in San Francisco, then a cafe in Stockholm. Their new post explains what they learned and why they are opening the platform behind those experiments (Pion) to anyone willing to hand a business to an agent. ...