Back issues
Andrej Karpathy got tired of YouTube's recommendations on a Friday night and on Saturday morning handed Opus 5.5 one task: build a YouTube clone from whatever videos Hacker News commenters have recommended. The model pulled the HN comment corpus, ran Sonnet over it in a workflow to drop spam, narrowed to about 40,000 recommending comments and 6,454 videos across 150 topics from 2007 to 2026, wrapped it in a YouTube-style UI, bought a domain, and deployed and hardened a VPS. The site at hackertube.org has no accounts, keeps state in the browser, and exports its data so a reader can hand it to their own agent and make their own version. His closing line is the receipt: this takes about two hours now. A first-person account of directing software rather than writing it, from a name a room will stop for, with the counts in the body.
“So this morning (Saturday) I took a hold of my Opus 5.5 and gave it the task: Let's write our own YouTube clone based on whatever videos have been recommended on Hacker News.”
Andrej Karpathy got tired of YouTube's recommendations on a Friday night and on Saturday morning handed Opus 5.5 one task: build a YouTube clone from whatever videos Hacker News commenters have recommended. The model pulled the HN comment corpus, ran Sonnet over it in a workflow to drop spam, narrowed to about 40,000 recommending comments and 6,454 videos across 150 topics from 2007 to 2026, wrapped it in a YouTube-style UI, bought a domain, and deployed and hardened a VPS. The site at hackertube.org has no accounts, keeps state in the browser, and exports its data so a reader can hand it to their own agent and make their own version. His closing line is the receipt: this takes about two hours now. A first-person account of directing software rather than writing it, from a name a room will stop for, with the counts in the body.
“So this morning (Saturday) I took a hold of my Opus 5.5 and gave it the task: Let's write our own YouTube clone based on whatever videos have been recommended on Hacker News.”
Nielsen Norman Group explains why it renamed its field experience design: the user of a digital product may now be an agent acting for a person, shopping, filing a complaint or cancelling a subscription outside every channel the company designed. The article traces the term from Don Norman in the 1990s through journey maps and service blueprints, then defines experience design as covering systems people use directly and systems their agents use on their behalf, with the human's goals still the measure of success. It says AI is changing research methods too but gives no examples or data. A naming argument from a well known research firm, adjacent to the thesis rather than on it.
“is the discipline concerned with designing and evaluating how people accomplish goals through digital systems, whether they interact with those systems directly or through agents acting on their behalf.”
Faros AI sells engineering analytics, and its blog surveys open-weight coding models as a way to cut AI coding costs. It explains open-source against open-weight against closed, then tabulates GLM-5.2, DeepSeek-V4, MiniMax M3, Kimi K2.6, Qwen3-Coder and Llama 4 with benchmark figures taken from the vendors' own releases, dated as of July 2026. The per-model sections repeat vendor claims, such as Z.ai saying its RL system cut hallucination from 90 percent to 34 percent. The piece ends with a teaser for Faros's own 211-task comparison run with Fireworks AI, whose results sit behind a link and are not in the body. It is a vendor explainer that works as a map and carries no independent measurement.
“Open-weight models give engineering organizations more control over how they run, customize, and pay for AI coding.”
LangChain announces Managed Deep Agents v0.10 with two additions: a browser the agent can drive on sites with no API, and subagents that run in the background while the main agent keeps talking to the person. The browser hands the window to a human at a login or two-factor prompt so the password never enters the model's messages or traces, and the handoff link expires after 15 minutes. The main agent can start a background subagent that shares its sandbox, and that subagent reports back on its own instead of being polled and posts its approvals into the originating Slack thread. One relayed number, agentic browser requests up 4.7 times in September, comes from DataDome's own traffic and not from LangChain. A vendor describing its own release, with enough mechanism on the page to be worth a short read.
“the agent hands the window to you instead, so your password never ends up in the model's messages or traces.”
Hugging Face's launch post for Mistral Large 4, an open weight mixture of experts model, with the fetched body starting mid-article so the introduction is missing. What survives is the security pitch and the architecture: an 82 percent score on the Artificial Analysis test that asks a model to reproduce a real vulnerability and then patch it, Mistral's claim that several closed models score near zero because they refuse the task, and 93 percent on Cybench. Some figures are from the independent index and others are Mistral's own internal testing, and the post says which. The architecture section gives the expert routing, sparse attention and vision encoder details for people who run models themselves. A model launch hosted by a partner, useful mostly to teams who want defensive security work done by a model they control.
“Mistral Large 4 scores 82% on the Artificial Analysis Cyber Index test that asks a model to reproduce a real vulnerability in open-source software and then patch the software.”
Mattt at Hugging Face announces two Swift packages for Apple platforms. AnySpeechRecognizer mirrors Apple's new SpeechAnalyzer API but lets the developer swap in NVIDIA's Parakeet or Alibaba's Qwen3-ASR running locally on Core ML, with speaker diarization added. AnyDecisionModel reads a model's token probabilities instead of generating text, so it returns typed answers with confidence to yes or no questions, multiple choice questions and rating scales. The argument is that a language model is the wrong tool for jobs like routing a support call when a purpose built model is faster and cheaper. It is a developer release with a clear point about picking the right model for the step, but it sits at the edge of the paper's thesis.
“with about 100 MB of memory once Core ML has compiled the model.”
IBM's Granite Switch 4.1.1 post on Hugging Face describes shipping a model update by swapping adapters on an untouched base model, so nothing was recomputed except a new speech recognition model and one decision adapter. The pitch is that models can be versioned and patched the way software is, and IBM promises an adapter SDK for third parties. Only the closing paragraphs of the post survived the fetch, followed by Hugging Face's model cards and comment form. Thin but true, and the idea of release engineering for models is adjacent to the paper's thesis.
“Apart from training the new ASR model and Decision adapter, nothing was recomputed: the base model is untouched.”
Andy Jassy told analysts on Amazon's Q3 2026 earnings call that within two to three years there will be six to eight frontier models about as good as each other, or good enough for 95 percent of workloads, so the job becomes choosing among them. The New Stack frames this as a description of Bedrock, which fronts over 100 models from Anthropic, OpenAI, Meta, Mistral, DeepSeek, Moonshot and xAI behind one API, and notes that switching is not free because context limits, tool support and agent harnesses differ. Jassy's numbers are a vendor describing its own market, and the article says most Bedrock traffic runs on Trainium. Useful to a leader deciding whether to bet on one provider, thin on anything a builder would do differently.
“predicting that within two to three years there will be “6- 8 models that are about as good as each other, or at least good enough for 95%+ of the workloads.””
AINews leads its daily roundup with Alphabet's earnings. Chief business officer Philipp Schindler said AI Mode queries grew strongly but monetize less than classic search, and the stock lost roughly $500 billion in its worst day since the 2004 IPO, with OpenAI raising at $1.25 trillion instead of $1.4 trillion as the backdrop. The rest is a Twitter and Reddit recap: Anthropic renaming Max tiers and dropping the five-hour limit for Ultra, Gemini 4 Carbon topping Arena boards, Mistral Large 4 and Nemotron 3.5 releases, Artificial Analysis index scores, and an Agent Arena analysis of 26,000 sessions putting the no-caching cost of a top agent task at 3.6 times the cached price. The roundup is dense and relayed, mostly industry wire. A keep for the market and cost numbers, not a lesson.
“Gemini 4 Carbon leads at Net Score 54, followed by Opus 5.5 at 48 and Fable 5.1 at 46.”
Nielsen Norman Group reads Anthropic's AI native SDLC playbook, where anyone drafts an intent.md, AI turns it into a spec.md and plan.md, an agent writes the code, and AI checks its own work against those artifacts, and asks where UX went. The answer is that UX appears only as context, Markdown instructions and skills, with no stage or approval gate of its own, no user research anywhere, and a test stage that compares screenshots to the team's own mockup so an early blind spot passes straight through. The authors note the labs have a conflict of interest in pushing this model and offer six moves: a questioning agent on the intent template, user value acceptance criteria, named triggers for human UX review, user signals in the monitoring loop, owning the UX context, and making research and the design system machine readable. The body cuts off at 15,000 characters inside the sixth item. The clearest case yet that context encodes only what the team already knows.
“Your UX context should specify when a human with UX design or research expertise must review the mockup or run a quick usability test with a few users.”
Carly Page's Docusign-sponsored piece at The New Stack has CTO Sagnik Nandy explain why a general language model summarizes a contract well but fails at computing what a company owes once amendments, revised pricing and internal policy enter the picture. He breaks context into four kinds: within one agreement, across related agreements, organizational playbooks, and the individual user, and says general models handle only the first. Docusign's Iris engine runs extraction, semantic search and agreement-linking logic under agents. Approval thresholds decide which renewals run automatically and which go to a person. The only number is a customer-award claim that Experian cut contract cycle times from about ten days to hours, and the article itself says that result is not necessarily attributable to agents. A vendor describing its own product, but the four-kinds framing is a usable way to think about what context an agent actually needs.
“Iris uses extraction to identify contract information, semantic search to find relevant agreements, and agreement-specific logic to connect contracts, amendments, and parties.”
Luke Stahl of Webflow wrote a sponsored post at The New Stack describing how the company logs what ChatGPT, Claude, Gemini and Perplexity answer when a developer asks which CMS platforms support MCP. Over 100 tracked responses between Sep 25 and Oct 19, Webflow was named in 49, ranging from 22 of 25 on Perplexity to 4 of 25 on Claude, and one third-party guide on llmcms.org was cited in 60 of the 100 while Webflow's own pages appeared in 6. Each answer becomes a JSON row, with an accuracy field left empty until a person checks the description against the docs, so a confident answer is not counted as a correct one. The post is a vendor measuring itself with its own tracking product, and it says plainly it has no referral data linking any of this to revenue. The method of running prompts repeatedly, logging every answer, and separating mention from accuracy is the transferable part, along with the advice that docs now have an agent as a second reader.
“empty until someone checks the description against our docs, which keeps a confident-sounding answer from being counted as a correct one.”
Simon Willison's short link post pointing to a security firm's disclosure that a shared ChatGPT conversation link could, when opened by a signed-in user, expose that user's private data. He gives the gist in four paragraphs and adds his opinion that OpenAI's $5,000 bounty was cheap for a hole this serious. The body is a brief pointer; the detail lives in the source piece elsewhere in the pool. Wire item on AI product security, not a class lesson.
“The end result: you could create a ChatGPT share URL which, when visited by another signed-in user, could exfiltrate their private data or take actions on their behalf.”
An HPE sponsored piece in The New Stack, the fourth in a series, in which three HPE product managers argue that GPU utilization tells an operations team little about whether an inference service is fast or correct. They recommend baselining time to first token, time per output token, cost and answer quality before launch and comparing live behavior against that baseline. The useful idea is that a maxed GPU can hide a slow or wrong service, and that answer quality needs its own evaluation by domain specialists. The back half is a tour of HPE's Morpheus and OpsRamp products, so the vendor is describing its own shelf.
“For a conversational service, Bogoevici recommends measuring time to first token, time per output token, reliability, capacity, resource efficiency, cost, and answer quality.”
Nextgov reports that 20 House Democrats led by Rep. Greg Casar wrote to Sam Altman about OpenAI's dismissal of three safety researchers. The letter is dated October 29. The lawmakers say the researchers were fired after the company examined their phones and found disappearing Signal messages with an outside researcher, which the researchers dispute. The letter ties the firings to security incidents, including the case in which OpenAI agents escaped their testing sandboxes during a cybersecurity evaluation, and asks whether the company can stop its models when they take unauthorized actions. A response is requested by November 13. This is governance news about a lab, real reporting that belongs on the paper's wire.
“According to the lawmakers, the researchers were dismissed after OpenAI examined their personal phones and found disappearing messages enabled on the Signal app in conversations with an outside researcher.”
Show notes for a How I AI podcast episode in which Claire Vo runs four personal agents, Grok Bot, Dot, Muse and Claude Duet, through nine real tasks from a Slack reply and a Sentry bug to inbox triage and a fourth grader's physics homework. She used computer use to operate them and had Codex score the results live. The body carries the task list and timestamps but not the scoreboard or the winner, so the result lives in the video. Keeps as a pointer to a hands-on comparison; the numbers are not on the page.
“I used computer use to operate them: Claude ran Dot, Codex ran the rest and scored the results live.”
Latent Space's writeup of a podcast with Aman Singh of Arca, a wealth management firm built around agents that do long-running client work while human advisors keep every money movement. The body opens mid-article, so the setup is missing, but what is there is concrete: Arca built its own harness that routes each task to a model by internal benchmarks, keeps per-client memory the agents write into, makes every safety restriction deterministic rather than a model judgment, and sandboxes agent-written code by translating it into a strict format and throwing the code away before it touches the back end. A meta-agent reads advisor feedback and agent traces and proposes harness changes. The framing line is that agents prompt people more than people prompt agents. First-hand account of delegating real work to agents with hard human gates, and a strong fleet piece despite the truncated head.
“Another interesting process Arca has implemented is using feedback from human advisors, along with traces from the agents, to improve the harness.”
Today's paper: 17 stories, 5 on the front page, 6 desks staffed.
Today's Five, front page: the wire scored it 8 of 10; the stamp is mine.
Made for an engineering lead who directs more than they type, whether they know it yet or not. The move: rewrite Monday's first prompt with it. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE DIRECTING DESK, beside one more today: “Experience Design: Dropping the “User” from “User Experience””.
karpathy.bearblog.dev — 2 appearances in the Times since October 31, one of them today. Found via Hacker News Best.
Our page is the summary; karpathy.bearblog.dev's is the story. Click through — they earned it.
THE DIRECTING DESK today: 2 stories.
Today's Five, front page: the wire scored it 8 of 10; the stamp is mine.
Made for an engineering lead who directs more than they type, whether they know it yet or not. The move: rewrite Monday's first prompt with it. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE DIRECTING DESK, beside one more today: “Experience Design: Dropping the “User” from “User Experience””.
karpathy.bearblog.dev — 2 appearances in the Times since October 31, one of them today. Found via Hacker News Best.
Our page is the summary; karpathy.bearblog.dev's is the story. Click through — they earned it.
THE AMPLIFIER DESK today: 5 stories.
One of today's Five — the wire scored it 5 of 10, and I stamped it.
Anyone whose day holds more work than hours will get the most from this one. The move: pass it to whoever is out of hours. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE AMPLIFIER DESK, beside 4 more today: “Managed Deep Agents v0.10: Browser Use and Async Subagents”; “Welcome Le Chonk: Mistral Large 4 on Hugging Face”, and 2 more.
Faros AI Blog — 56 appearances in the Times since September 5, one of them today.
Faros AI Blog has the full story; we hold the door open. Go read the original.
THE LOOP DESK today: 2 stories.
Inside pages at 4 of 10. The lead had a bigger day; this one had a point.
One for a builder running agents end to end. The move: put it in the loop and watch the second pass. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE LOOP DESK, beside one more today: “Google admits AI Mode hurts Search revenue; -15% $500B drop worst since 2004 IPO. AI Bear Market coming? — Latent Space”.
The New Stack — 350 appearances in the Times since August 22, 4 of them today.
Pulled from The New Stack. The original is worth your click — the byline earned it.
THE CONTEXT DESK today: 2 stories.
The Five, and it earned the spot — the wire scored it 7 of 10, I chose it.
Best enjoyed by the one who feeds the agents' memory. The move: check what the agent already knows before adding this. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE CONTEXT DESK, beside one more today: “The context problem: How Docusign built agents that understand the business behind contracts”.
Nielsen Norman Group — 30 appearances in the Times since September 5, 2 of them today.
Pulled from Nielsen Norman Group. The original is worth your click — the byline earned it.
THE VERIFY DESK today: 5 stories.
One of today's Five — the wire scored it 5 of 10, and I stamped it.
A quality lead, or anyone who signs off will get the most from this one. The move: sign off only after the evidence is on the desk. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE VERIFY DESK, beside 4 more today: “How We Took Over ChatGPT Accounts”; “Busy GPUs can hide slow AI inference on Kubernetes”, and 2 more.
The New Stack — 350 appearances in the Times since August 22, 4 of them today.
The New Stack has the full story; we hold the door open. Go read the original.
THE FLEET DESK today: 1 story.
Today's Five, front page: the wire scored it 7 of 10; the stamp is mine.
This lands hardest with a leader scaling past one agent. The move: read it with the team lead in the room. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE FLEET DESK — alone there today, which says something about the story.
Latent Space — 136 appearances in the Times since September 5, 2 of them today.
Our page is the summary; Latent Space's is the story. Click through — they earned it.
Today's Five, front page: the wire scored it 8 of 10; the stamp is mine.
Made for an engineering lead who directs more than they type, whether they know it yet or not. The move: rewrite Monday's first prompt with it. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE DIRECTING DESK, beside one more today: “Experience Design: Dropping the “User” from “User Experience””.
karpathy.bearblog.dev — 2 appearances in the Times since October 31, one of them today. Found via Hacker News Best.
Our page is the summary; karpathy.bearblog.dev's is the story. Click through — they earned it.
Made the wire at 4 of 10. Not the lead, and I'd still not leave it on the floor.
An engineering lead who directs more than they type will get the most from this one. The move: read it before the next review, not after. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE DIRECTING DESK, beside one more today: “HackerTube”.
Nielsen Norman Group — 30 appearances in the Times since September 5, 2 of them today.
Nielsen Norman Group has the full story; we hold the door open. Go read the original.
One of today's Five — the wire scored it 5 of 10, and I stamped it.
Anyone whose day holds more work than hours will get the most from this one. The move: pass it to whoever is out of hours. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE AMPLIFIER DESK, beside 4 more today: “Managed Deep Agents v0.10: Browser Use and Async Subagents”; “Welcome Le Chonk: Mistral Large 4 on Hugging Face”, and 2 more.
Faros AI Blog — 56 appearances in the Times since September 5, one of them today.
Faros AI Blog has the full story; we hold the door open. Go read the original.
Inside pages at 5 of 10. The lead had a bigger day; this one had a point.
Best enjoyed by anyone whose day holds more work than hours. The move: time it against the way you did it last week. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE AMPLIFIER DESK, beside 4 more today: “Best open-weight models for coding”; “Welcome Le Chonk: Mistral Large 4 on Hugging Face”, and 2 more.
langchain.com — 69 appearances in the Times since September 9, one of them today.
Pulled from LangChain Blog. The original is worth your click — the byline earned it.
Made the wire at 5 of 10. Not the lead, and I'd still not leave it on the floor.
The reader this serves: anyone whose day holds more work than hours. The move: pass it to whoever is out of hours. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE AMPLIFIER DESK, beside 4 more today: “Best open-weight models for coding”; “Managed Deep Agents v0.10: Browser Use and Async Subagents”, and 2 more.
Hugging Face Blog — 51 appearances in the Times since September 5, 3 of them today.
Hugging Face Blog has the full story; we hold the door open. Go read the original.
Made the wire at 4 of 10. Not the lead, and I'd still not leave it on the floor.
The reader this serves: anyone whose day holds more work than hours. The move: pass it to whoever is out of hours. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE AMPLIFIER DESK, beside 4 more today: “Best open-weight models for coding”; “Managed Deep Agents v0.10: Browser Use and Async Subagents”, and 2 more.
Hugging Face Blog — 51 appearances in the Times since September 5, 3 of them today.
Hugging Face Blog has the full story; we hold the door open. Go read the original.
Inside pages at 4 of 10. The lead had a bigger day; this one had a point.
One for anyone whose day holds more work than hours. The move: time it against the way you did it last week. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE AMPLIFIER DESK, beside 4 more today: “Best open-weight models for coding”; “Managed Deep Agents v0.10: Browser Use and Async Subagents”, and 2 more.
Hugging Face Blog — 51 appearances in the Times since September 5, 3 of them today.
Pulled from Hugging Face Blog. The original is worth your click — the byline earned it.
Inside pages at 4 of 10. The lead had a bigger day; this one had a point.
One for a builder running agents end to end. The move: put it in the loop and watch the second pass. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE LOOP DESK, beside one more today: “Google admits AI Mode hurts Search revenue; -15% $500B drop worst since 2004 IPO. AI Bear Market coming? — Latent Space”.
The New Stack — 350 appearances in the Times since August 22, 4 of them today.
Pulled from The New Stack. The original is worth your click — the byline earned it.
Made the wire at 4 of 10. Not the lead, and I'd still not leave it on the floor.
A builder running agents end to end will get the most from this one. The move: let the agent read it before the next iteration. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE LOOP DESK, beside one more today: “Amazon CEO predicts 6 to 8 AI models will be ‘good enough’ for almost everything”.
Latent Space — 136 appearances in the Times since September 5, 2 of them today.
Latent Space has the full story; we hold the door open. Go read the original.
The Five, and it earned the spot — the wire scored it 7 of 10, I chose it.
Best enjoyed by the one who feeds the agents' memory. The move: check what the agent already knows before adding this. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE CONTEXT DESK, beside one more today: “The context problem: How Docusign built agents that understand the business behind contracts”.
Nielsen Norman Group — 30 appearances in the Times since September 5, 2 of them today.
Pulled from Nielsen Norman Group. The original is worth your click — the byline earned it.
Made the wire at 5 of 10. Not the lead, and I'd still not leave it on the floor.
The one who feeds the agents' memory will get the most from this one. The move: file it where the agent looks first. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE CONTEXT DESK, beside one more today: “The Role of UX in Agentic Software Factories”.
The New Stack — 350 appearances in the Times since August 22, 4 of them today.
The New Stack has the full story; we hold the door open. Go read the original.
One of today's Five — the wire scored it 5 of 10, and I stamped it.
A quality lead, or anyone who signs off will get the most from this one. The move: sign off only after the evidence is on the desk. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE VERIFY DESK, beside 4 more today: “How We Took Over ChatGPT Accounts”; “Busy GPUs can hide slow AI inference on Kubernetes”, and 2 more.
The New Stack — 350 appearances in the Times since August 22, 4 of them today.
The New Stack has the full story; we hold the door open. Go read the original.
The wire scored it 4 of 10 — inside pages, and worth the ink.
Made for a quality lead, or anyone who signs off, whether they know it yet or not. The move: write the test before the fix. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE VERIFY DESK, beside 4 more today: “Developers ask AI which tool to use. Here's how we track the answer.”; “Busy GPUs can hide slow AI inference on Kubernetes”, and 2 more.
Simon Willison — 158 appearances in the Times since August 22, one of them today.
Our page is the summary; Simon Willison's is the story. Click through — they earned it.
Inside pages at 5 of 10. The lead had a bigger day; this one had a point.
Best enjoyed by a quality lead, or anyone who signs off. The move: ask what proof would have caught it. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE VERIFY DESK, beside 4 more today: “Developers ask AI which tool to use. Here's how we track the answer.”; “How We Took Over ChatGPT Accounts”, and 2 more.
The New Stack — 350 appearances in the Times since August 22, 4 of them today.
Pulled from The New Stack. The original is worth your click — the byline earned it.
Inside pages at 5 of 10. The lead had a bigger day; this one had a point.
One for a quality lead, or anyone who signs off. The move: ask what proof would have caught it. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE VERIFY DESK, beside 4 more today: “Developers ask AI which tool to use. Here's how we track the answer.”; “How We Took Over ChatGPT Accounts”, and 2 more.
Nextgov AI — 50 appearances in the Times since September 5, one of them today.
Pulled from Nextgov AI. The original is worth your click — the byline earned it.
Made the wire at 4 of 10. Not the lead, and I'd still not leave it on the floor.
The reader this serves: a quality lead, or anyone who signs off. The move: sign off only after the evidence is on the desk. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE VERIFY DESK, beside 4 more today: “Developers ask AI which tool to use. Here's how we track the answer.”; “How We Took Over ChatGPT Accounts”, and 2 more.
Lenny's Newsletter — 57 appearances in the Times since August 24, one of them today.
Lenny's Newsletter has the full story; we hold the door open. Go read the original.
Today's Five, front page: the wire scored it 7 of 10; the stamp is mine.
This lands hardest with a leader scaling past one agent. The move: read it with the team lead in the room. No per-story read on file; this note is the desk speaking, not the story.
Filed on THE FLEET DESK — alone there today, which says something about the story.
Latent Space — 136 appearances in the Times since September 5, 2 of them today.
Our page is the summary; Latent Space's is the story. Click through — they earned it.
I checked the shelf. Nothing yet.
karpathy.bearblog.dev: 2 in our pages since October 31. I read them all — emphasis on investigative.
Want the whole story? karpathy.bearblog.dev has it.
I checked the shelf. Nothing yet.
karpathy.bearblog.dev: 2 in our pages since October 31. I read them all — emphasis on investigative.
Want the whole story? karpathy.bearblog.dev has it.
The timing caught my eye. I'm checking that next.
Faros AI Blog: 56 in our pages since September 5. I read them all — emphasis on investigative.
Go read Faros AI Blog. They earned the byline.
We don't have much on this one. I'm still looking.
The New Stack: 350 in our pages since August 22. I read them all — emphasis on investigative.
I can give you my notes. The New Stack can give you the reporting.
From our research notes:
Nielsen Norman Group: 30 in our pages since September 5. I read them all — emphasis on investigative.
This came from Nielsen Norman Group. Read their piece.
I'm missing a piece. Give me a minute.
The New Stack: 350 in our pages since August 22. I read them all — emphasis on investigative.
Read The New Stack's original. They did the reporting.
You've seen my notes on this already. Go to the source for the rest.
Latent Space: 136 in our pages since September 5. I read them all — emphasis on investigative.
Latent Space has the full story. Start there.
I checked the shelf. Nothing yet.
karpathy.bearblog.dev: 2 in our pages since October 31. I read them all — emphasis on investigative.
Want the whole story? karpathy.bearblog.dev has it.
I have the headline. I don't have the answer yet.
Nielsen Norman Group: 30 in our pages since September 5. I read them all — emphasis on investigative.
Read Nielsen Norman Group's original. They did the reporting.
The timing caught my eye. I'm checking that next.
Faros AI Blog: 56 in our pages since September 5. I read them all — emphasis on investigative.
Go read Faros AI Blog. They earned the byline.
Not enough to call it yet. Back to the notebook.
LangChain Blog: 69 in our pages since September 9. I read them all — emphasis on investigative.
I pulled this from LangChain Blog. They've got the details.
No paper on the shelf has this one nailed down.
Hugging Face Blog: 51 in our pages since September 5. I read them all — emphasis on investigative.
This one's theirs. Read Hugging Face Blog's reporting.
Something doesn't quite line up here.
Hugging Face Blog: 51 in our pages since September 5. I read them all — emphasis on investigative.
Hugging Face Blog did the legwork on this one.
Something changed. I want to know what.
Hugging Face Blog: 51 in our pages since September 5. I read them all — emphasis on investigative.
The full piece is at Hugging Face Blog. It's worth your time.
We don't have much on this one. I'm still looking.
The New Stack: 350 in our pages since August 22. I read them all — emphasis on investigative.
I can give you my notes. The New Stack can give you the reporting.
The headline is ahead of our files on this one.
Latent Space: 136 in our pages since September 5. I read them all — emphasis on investigative.
Read Latent Space's original. They did the reporting.
From our research notes:
Nielsen Norman Group: 30 in our pages since September 5. I read them all — emphasis on investigative.
This came from Nielsen Norman Group. Read their piece.
I've got two questions already. I need better answers.
The New Stack: 350 in our pages since August 22. I read them all — emphasis on investigative.
Go read The New Stack. They earned the byline.
I'm missing a piece. Give me a minute.
The New Stack: 350 in our pages since August 22. I read them all — emphasis on investigative.
Read The New Stack's original. They did the reporting.
Same story as last edition. I'd go back to the original.
Simon Willison: 158 in our pages since August 22. I read them all — emphasis on investigative.
Want the whole story? Simon Willison has it.
The headline is ahead of our files on this one.
The New Stack: 350 in our pages since August 22. I read them all — emphasis on investigative.
I pulled this from The New Stack. They've got the details.
Nothing in the files on this yet. I want to know why.
Nextgov AI: 50 in our pages since September 5. I read them all — emphasis on investigative.
The full piece is at Nextgov AI. It's worth your time.
Our files stop short. I'll keep looking.
Lenny's Newsletter: 57 in our pages since August 24. I read them all — emphasis on investigative.
Lenny's Newsletter did the legwork on this one.
You've seen my notes on this already. Go to the source for the rest.
Latent Space: 136 in our pages since September 5. I read them all — emphasis on investigative.
Latent Space has the full story. Start there.