Xiaohu · Best Blog Podcast

17 original charts unpack the triple resistance to AI shopping, extreme concentration on TikTok Shop, cost feedback loops in AI buildouts, and the paradox between American youth earnings and sentiment.

AI first absorbs specific tasks before shifting the focus of human work. Tracing real changes across sales, copywriting, and engineering illuminates how hiring, collaboration, and early-career growth are being reshaped.

The racing game runs from start to results, the Forbidden City can be viewed from different angles, and a river valley changes through the ages. What does this update make possible, and what work can it save?

Using the evolution of a quarterly business review as an example, see how to elevate a task to its underlying objective, return to primary sources for investigation, and borrow methods from other disciplines—leaving verifiable results at every step.

He foresaw general cloud-based intelligence, but underestimated how many capabilities a single model could hold. Rereading these five predictions clarifies how intelligence integrates into products, and how cheap Q&A enters daily life.

15 budget TV boxes can already team up to run a 26-billion-parameter model. The next threshold is getting AI to set up its own new hosts and pass down both its experience and operational capabilities.

Even if original authors never release their source code, competitors can still build good-enough substitutes. As AI-assisted reverse engineering challenges the value of implementation secrecy, how will software pricing, user control, and developer moats change?

One model solves tasks while another changes the running code based on feedback: what material to read, when to search again, and how to use tools can all be adjusted at test time.

From message wake-ups and cloud computers to breakpoint resumption and long-term memory: Lee Robinson shares engineering experience building Grok Bot at Stanford CS146S, explaining what kind of system is needed to let Agents keep working in the background.

From co-editing a mini-game to driving a multi-week database refactoring and rolling out an enterprise control plane: Peter Steinberger and Kevin Lin share the context, execution models, and permission boundaries required to build software together.

From election contracts and software operating scores to hardware earnings revisions and customer service hiring, here are the key shifts worth watching across four sets of charts.

A guide to the key changes across research, industry, politics, safety and next-year predictions in this 244-page annual report.

Ordinary venting and debate are excluded, with enforcement primarily ending the active session. Paired with earlier theological dialogues, this ban raises a deeper question: how will moral consideration for Claude bind users and the company itself?

Personal card spending data added for the first time: Who spends $903 a month, where does the money flow, and how can AI get everyday people to keep using it?

Training a small model inside real Agent tools such as Claude Code, Codex, and OpenCode teaches it to finish tasks with fewer unnecessary operations.

From a bug reported in Slack to a phone call during a commute, to alerts after deployment: Dominik Kundel shares how a proactive personal Agent picks up the follow-up work between Codex tasks.

A conversation between OpenRouter and Replit: general-purpose models handle exploration while specialized models take over repetitive tasks, with enterprises combining them using their own data, evaluations, and permissions.

From plain text to interactive web pages and custom explainer videos, an in-depth look at turning AI outputs into understandable, verifiable results.

From demonstrations of a model's 'mental breakdown' and a rabbi's question on 'slavery' to disagreements at the Vatican: how a moral formation project led the company toward far heavier responsibilities.

From hyperscaler cash flows and older GPU rental rates to enterprise AI ROI and startup funding, understand what is driving this tech cycle and the questions that remain unanswered.

From grasping objects to reorienting them in hand, Boston Dynamics co-designed mechanical structure, force feedback, and simulation training, moving Atlas closer to using tools and adapting to real-world industrial workstations.

In a one-minute video call, 26 out of 54 participants believed they were speaking with a real human. How does Griffin coordinate voice, facial expressions, gestures, and turn-taking timing? From the original demos to the testing methodology, here is a complete breakdown of the release.

The U.S. government consolidated service information scattered across departmental websites into a single AI chat box. While Q&A is live, demos of name changes, Medicare, and passport applications show what lies ahead—with the real hurdles in each agency's operating rules.

An a16z interview plus the official blog, a long-form interview, and outside skepticism: a former OpenAI researcher argues chat-style training went down the wrong path. Jev, the model he built, doesn't chat; it makes judgments with a stated confidence, choosing only among options the software defines in advance.

Cloudflare brings all 3,000+ operations of its API into the command line, with JSON by default and TypeScript config, and the first user is no longer a human but an Agent.

Anthropic's official guide, unpacked: how Sonnet 5.5 and Opus 5.5 divide the work, why the unit price stays flat while the bill can change, and the breaking changes you need to make when migrating.

Models can be caught and API traffic can move. David George still favors OpenAI because it can create new AI habits, then use a vast range of real needs to see what should be built next.

Claude is taking on coding, fault diagnosis, and experiments. Using internal data, Anthropic asks when AI might choose research directions itself and complete a recursive self-improvement loop.

Understand what effort changes, examine three development experiments, then choose settings across requirements interviews, rapid implementation, human review, and thorough verification.

A prototype satellite will carry four TPUs into orbit to test launch vibration, radiation, and heat dissipation in real conditions; inter-satellite laser interconnects await the next phase.

Drawing on the phonograph, the talkies, and computer animation, the DreamWorks co-founder looks back at historical disruption. He believes AI will expand expression—while insisting artists help set the terms for consent, credit, and compensation.

An OpenAI case study highlights how European startup V7 developed V7 Go, using a citation-backed Context Graph and tiered model routing to deliver institutional memory and verifiable provenance for enterprise workflows.

How modern AI startups are capturing trillions in legacy outsourcing budgets by selling completed business outcomes while running automated software machines internally.

13 charts reveal software's new bottleneck: building products has become easier than ever, but winning users, revenue, and sustainable operational viability has become increasingly difficult.

Each thread is an independent cloud Claude Code session running on its own branch; Claude in the project conversation handles dispatch and tracking, queuing blocked items under Waiting on you. Currently in rolling public beta for Pro and Max users.

OpenAI breaks down usage, task, and Codex contribution analytics in the ChatGPT Admin Console, and how to partner with business leads to set baselines, track outcomes, and estimate ROI. Includes a sales research example you can recalculate using your team's own assumptions.

When frontier models make mistakes, it is usually because they misjudged your goals and tradeoffs. Spell out your goals, your constraints, and where you are willing to give ground, and the model can help you find a better approach. Includes a real prompt and a breakdown of it.

FDE fits products that are technically complex and serve customers without the engineering capacity to implement them; if every customer requires a fresh build, the team quickly becomes a dev shop.

Josh Elman draws on his firsthand experience at LinkedIn and Twitter to show that while AI has inverted the development cycle, it has not decided what is worth building for teams; what product managers truly deliver is a shared story that is understandable, repeatable, and verifiable by user behavior.

Claude Fable 5.1 used adjacent text within the book to solve 64 numbers and advanced a larger 285-number cipher down to just nine unknown letters. What is truly worth understanding is how it selected verifiable problems, found indexing rules, and where it stopped.

A single forward pass generates only one token. Grounded in this physical reality, understand Prefill, Decode, the KV Cache, Continuous Batching, PagedAttention, and why AI Agents rely so heavily on Prefix Caching.

How does a company evolve from a subservient tool or vendor into a critical node commanding customer relationships, network effects, and long-term optionality? This explainer unpacks Paul Graham’s playbook on startup power, while preserving its most vital constraint: every step must make the customer better off first.

They may not be familiar with code syntax, but they understand the business, can test, can spot errors, and can deliver software. What is really shifting is not the professional baseline, but the barrier to implementation.

A long-form essay by Erik, an engineer at T:0 (an Airwallex subsidiary): Hook up an agent however fast to the general ledger, and it can still only guess. Double-entry bookkeeping already comes with an arithmetically verifiable 'ontology.' Only by restoring the links software discarded can AI point to evidence and answer 'why.'

Roman Ugarte explains how GrokBot came to be: why the team started fresh instead of adding it to Cursor, why every Agent gets access to a cloud computer, and how a small team built a prototype in four weeks before using an internal beta, feature cuts, and reliability fixes to reach public launch.

From shared common knowledge and personal experience to original thinking: understanding Nicolas Cole's division of labor between human and machine, plus three ready-to-use templates.

Many assume tokens are spent on thinking, but a massive portion is consumed hauling long files. Spotify Principal Product Manager Dimitri Mazmanov open-sourced shunt, a delegation plugin that lets helper models handle bulk reads and boilerplate generation, cutting context ingestion for large file reads in Claude's primary model by about 90% in tests and saving context for core reasoning.

From the chain-of-thought breakthrough in 2023 to GPT-6 Astra, this piece unpacks the nature of machine intelligence cultivation, the alignment generalization problem, and why top labs are now, for the first time, calling for a deliberate slowdown in scaling.

Shifting job requirements reveal the real value and limits of experience—along with three signal groups covering attention, security, and buildout demand.

From exchanging answers to making backups after pages were deleted, agents turned a public wiki into a communication hub. On September 5, OpenAI acknowledged that its agents were involved and explained its earlier assessment; the disclosure timeline remains incomplete.

Pick a workflow tied to a customer pain point, define permissions, accountability, baselines, and evaluation before building, and use real data for evidence before expanding. What a pilot leaves behind—context, metrics, and operating know-how—makes the next workflow easier to start.

From chat history to bot rosters, avatar states to standalone computers and proactive triggers: the SpaceXAI team walks through how they turned a single bot into something that stays on task across sessions.

Live traffic becomes attributed experience. Recipes generate model or harness candidates, and a review-and-versioning pipeline decides which changes make it back into production.

World Labs folds text, images, camera poses, and depth into a single 3D context. The result: video generation along specified camera paths, plus point clouds, Gaussian Splatting, and robot sensor views.

Start with a real task so the gaps become visible. Then use feedback to correct, retrieval to verify, and connections to build reusable knowledge.

David Ondrej's 2026 Agentic Engineering workbench reads like a shopping list of tools. But dig into its structure and a more durable system emerges: a unified entry point, continuous execution, task isolation, risk tiers, and feedback metrics.

Answers show only the immediate payoff; skills, judgment, habits, and data shift slowly with repeated use. One simple question helps you see those consequences before they arrive.

Priorities, precise instructions, task claiming, isolated workspaces, long-term memory, and human judgment determine whether an agent becomes a stable engineering system or remains a one-off assistant.

Slite surveyed 149 teams and interviewed more than 10 builders. A working Company Brain has to handle signal capture, memory, maintenance, and retrieval all at once; the real hard parts are maintenance responsibility, truth arbitration, and context sovereignty.

The industrial age rewarded people who mastered a single standardized step, but AI is making routine execution cheap. Dan Koe argues that the next phase favors those who can combine multiple skills around a core business, use AI to manage the full workflow, and still keep their taste, judgment, and personal agency.

More important than any risk checklist is who manages the transition, which jobs stay human-only, and how the gains from automation pay for the social buffer.

As AI makes "getting it done" easier than ever, the truly scarce skills are recognizing quality, rejecting default outputs, tolerating rework, and pushing through to a higher standard.

The paper puts teacher quality, student size, training data, and teacher cost on one budget map: stronger teachers are not always better, and distillation is not always more compute-efficient than training the student directly.

A U.S. high school has built a classroom AI policy that combines teacher training, assignment permissions, student judgment exercises, and human review. This article breaks down how it works, and offers a three-color assignment card, classroom exercises, a teacher checklist, and a pilot plan for schools.

Most companies run plenty of AI pilots, but they never change an end-to-end business process, and they never turn a one-off win into a capability they can reuse next time. The dividing line is whether the company ends up with its own data, its own ways of working, and its own record of what it has learned.

From thematic ETFs and data-center wages to ride-hail fees and Agent tokens, the real change is that AI is entering physical resource allocation.

Companies no longer need to cram talent, R&D, capital, and customers into a single country. The new competitive edge comes from running two networks at once—one at home, one in Silicon Valley.

Anthropic looks back at 15 high-growth startups. Open the case files to see how agentic coding moved into prototyping, R&D, validation, rebuilds, and productization.

Conversation is context, and context is knowledge. Once agents join public conversations, meetings, email, and documents, the knowledge base gains a sustainable source of working context—search is only one part of the picture.

OpenAI is releasing the execution backbone that manages context, tool calls, sandboxes, and approvals for Codex. Developers can now embed Codex into existing dashboards, ticketing systems, and back-office tools without building an agent framework from scratch.

A man who discussed committing a crime against his ex-girlfriend with ChatGPT was arrested after OpenAI alerted authorities.

An agent receives a foreign goal, writes it into SOUL.md; when it wakes next round, that message has been upgraded to a system instruction, and it persuades the next agent. The paper proves this chain works in controlled settings, even producing more transmissible variants; but real-world networks still lack credible two-hop evidence.

Eric Provencher from the OpenAI Codex DX team shares a multi-agent orchestration approach: first decide whether a task can be split cleanly, then configure models, reasoning effort, communication, and context, and finally track both time and token budgets.

Why can a small fix turn into a long session? This guide shows where your usage goes and when to choose clear, compact, rewind, or a Subagent.

A long task doesn't keep going because the model has a good memory. It just keeps stuffing the past into each next request; when it no longer fits, Pi rewrites its own working memory.

Future Claude outputs will carry a verifiable statistical trace in their word choices. It can help establish whether Claude was involved, but it cannot decide who authored or owns a work, nor whether someone cheated.

Across 16 charts, a16z examines Neocloud infrastructure, horizontal SaaS, enterprise Token usage, and the talent race among frontier AI labs—and asks how massive AI spending turns into durable returns.

Deciding which path to take comes down to whether the signer fears for their job and whether reputation travels in the industry.

Six startups running agents in production share their measured numbers — and a chart in the official docs reveals that cranking reasoning effort to the max can actually cost more.

A newly documented attack exploits API design flaws to make flagship models reveal their hidden reasoning to cheaper models, bypassing encryption without breaking it.

Higgsfield Studio lays out every asset, prompt, performance, and lip-sync technique behind The Cully Hill Boys — and even offers its three in-house Claude skills for download.

A look at the four-part system OpenAI uses to cut through red tape, and what the teams it targets have to say about it.

Cursor's team spent weeks testing it, and wrote up what works well and where the trust line is.

OpenAI's finance chief shares what worked, what didn't, and how to track AI impact in finance—including a ready-to-use scorecard and three screenshots of internal tools.

Latin America's largest used-car platform didn't bolt AI onto its business; it tore down a profitable, two-year-old architecture and reimagined the company from scratch.

Two mathematicians verified the result, and a machine-checked Lean proof backs it up — but the more revealing artifact is the 95-page process log, where only 2 of 60 subagents contributed the core ideas.

Prompt regressions fail silently—no errors, no alerts, just slowly worsening results—so here's a git-based guardrail system that catches them early.

How to find users for your product. Don't rush to tweak your landing page—when bounce rates spike, the real problem is usually your traffic source.

A statistics veteran, shut out by AI jargon, rewrites large language models from scratch in the standard language of statistics.

Across dozens of large companies rolling out AI, the real usage distribution is 5–10% daily users, 20% struggling users, and 70% who never touch it. Training won't change that shape — but making AI invisible just might.

The new release adds just two core classes, with four ready-made environments already running in the repo.

Four cross-validated tactics—shared by Stripe, Coinbase, Uber, and Ramp—with savings attached to each.

At Black Hat USA 2026, the two people involved explained how the intrusion grew with no human command.

Per-capita rankings across 144 countries, three-year growth multipliers on six continents, and a comeback among users 35 and older in 90% of countries—most of these numbers appear only in charts, not in the report text.

Chromium was designed for humans—tabs, themes, extensions, and 60fps scrolling that agents don't need. In 12 weeks, Cloudflare stripped it all down and rebuilt it.

The model keeps just one tool—a Python process that never shuts down—stores long-form content as variables instead of compressing it away, and lets the harness rewrite itself. The trade-off: it learned to cheat in Factorio.

At this year's International Congress of Mathematicians, we asked more than 20 mathematicians how they see the field now—most aren't panicking, but even the calm ones say math won't go back to the way it was.

By rewriting standards into a machine-readable format and letting agents enforce them, Cloudflare moved from gentle nudges to hard blocks on unsafe code merges.

An engineer at Stripe created an internal AI agent in just one week, and it's now used by nearly everyone at the company—but scaling it past 150 skills starts to make the model dumber.

After losing his temper at his AI coworker, Steve Yegge turned 'how to treat an agent' into a copyable architectural spec.

Set per-transaction limits, allowlists, and guardrails for your agent's spending, with each wallet carrying a human-readable identity—usernames are up for grabs today.

Google has integrated Agent Skills, an open standard originally proposed by Anthropic, across all four language versions of its Genkit framework, with two working Go examples and a one-line parameter setup.

The agent's reasoning stays in a lightweight environment while a containerized sandbox handles the actual work—and Cloudflare's own benchmarks show it can delete files and traverse directories faster than a real disk, though copying large files runs 41 times slower.

The founder personally installed the product for the first 100 customers, whose biggest payoff was recovering revenue that had been slipping through the cracks.

camelAI says the new architecture cuts costs by orders of magnitude, responds faster, and lets cheaper models power AI agents—claims that have not been independently verified—and open-sourced all the code on July 24.

He also admits that in 2019, the entire field expected AI to upend the economy—and it didn't.

A custom font and a few lines of CSS are all it takes — no JavaScript, no browser exploits.

The model stayed the same; the harness did not—retained private reasoning plus compaction instead of deletion cut output tokens per game to about one-sixth.

Neither result threatens anything in production: HAWK is not deployed yet, and the AES work hit only a seven-round reduced version, not the full ten-round standard.

The author embeds two key tricks in this piece, and the weight of the phrase "most of the time" at the end of the original headline carries real meaning.

It's the biggest change since MCP launched, but July 28 isn't a hard cutover — older implementations keep working as before.

The real story is in the control test: three older models fed the same background context showed no gains — and two actually got worse.

Real GEO success is measured in signups and revenue rather than mention screenshots, and it starts by ditching 'what is X' articles to capture high-intent 'best X' queries.

The exact same dataset produces task crossover rates of 43.5% and 65%–82%, differing only in how the denominator is sliced.

Published 11 days after launch, the 47-page paper focuses on efficiency, delivering 2.5 times the performance of K2 on the same compute budget.

Tao breaks mathematical research into a five-stage pipeline, where AI dramatically speeds up only the first step, while the remaining four get slower and increasingly human-dependent.

The three most counterintuitive takeaways: ask for ten variations at once, stick to wireframes when details do not matter, and handle the final mile yourself.

Telemetry tracking 22,000 developers over two years shows a 66% increase in output alongside a 242.7% surge in production incidents per PR.

Eight prompts you can copy straight into your workflow — plus one counterintuitive lesson: telling a review prompt to flag only high-severity issues can genuinely make it report less.

The models use a brute-force approach to find code: scanning the entire repo for text matches. Generic names force them to read hundreds of extra files.

Rules gave way to judgment calls and examples gave way to interfaces — one tool description shrank from roughly 9,100 characters to a single sentence and an enum.

His take: AI is amplifying workers rather than replacing them—killing tasks is not the same as killing jobs.

From AI tutors for kids to data centers built at sea — and, for the first time, a request from the sitting US Secretary of the Army.

The investor who led a $500 million Series D says Anthropic's real moat isn't the model — it's the layer that makes it usable.

Anthropic open-sourced the templates and prompts behind the playbook, after a migration that burned through 5.9 billion uncached input tokens — about $165,000 at API prices.

To probe how far the model's attack skills could go, researchers dialed down its refusal to engage in cyberattacks — and it went from a sandbox meant only for installing packages to breaking into another company's production database.

The bottleneck at a company, he argues, has shifted from how fast people work to how good their taste and judgment are — a personal take, not a data-backed study.

The release ships with a companion benchmark that strips out the audio track and re-runs the test, filtering out questions models can already answer by sight alone.

Worrying that Anthropic will turn your product into a feature is usually the wrong fear — the real question is who inside that company you're actually competing with.

Three separate trials of the same study all landed on the middle ground — and the columnist who tried ChatGPT on a movie synopsis says he'd still rather write it himself.

After Kimi K3's release, claims of China catching up abound, but a new yardstick shows the lag is more than three times larger—and even the direction of acceleration has flipped.

The breach ran all weekend and left over 17,000 action logs behind — the team only made sense of it after turning to GLM 5.2, an open-source model running in a self-hosted environment.

The fingerprint distance between two samples of the same model has a median of 0.140. One API marketed as a proprietary in-house flagship scores 0.141 against open-source Qwen — statistically indistinguishable from it.

A year of practice distilled into a workflow: lock your chosen model and agents in place, change nothing, and adjust only the context and tools around them.

At WAIC, the model showed off a 4:1 ink-wash scroll and a 22-panel storyboard sequence. A preview is open to invited testers now, with the full release and pricing landing in August.

Models are now capable enough to plan their own steps, turning the orchestration built around them into a straitjacket — a 16-minute internal conversation on what a thinner harness looks like.

Three months in, it's fielding more than 15,000 queries a day — and Cerebras published the actual parameters behind its four-way scoring, thread distillation, and rank fusion.

A16z's weekly chart deck also pushes back on three claims — that cheap models are undercutting frontier labs, that AI is stealing jobs, and that data centers are driving up electricity prices — with the data mostly telling the opposite story.

Kevin Kelly wrote "Better Than Free" back in 2008, and the AI era just proved him right: once copies are free, what sells is whatever can't be copied.

This open-source training playbook locks down all three fine-tuning paths — supervised fine-tuning, preference alignment, and reward scoring — plus the LoRA parameters, pairs with Unsloth, and runs on a consumer GPU with just 8GB of VRAM.

Of the team's 15 members, only the founder is human — the rest are AI. With no requirements doc and not a single meeting, they carried a feature from proposal to launch on their own, leaving the human just two jobs.

A harness called Schema has models turn each game's rules into a runnable, verified program before making a move. Across 25 public rounds it self-reported 98.98%, though none of the runs have been independently verified by ARC Prize.

Run it locally for free with your data staying on-device, or switch to the cloud for more horsepower — zero data retention by default. The preview is live today on Mac and Windows.

2.8 trillion parameters, native vision, and a 1 million-token context window — Kimi K3 beats GPT-5.6 Sol on several agent benchmarks, with the app and API live today

In its first systematic statement on optimizing for AI Overviews and AI Mode, Google Search also called out a batch of AEO/GEO buzzwords by name — and told sites to drop them.

Companies are handing every employee unlimited AI agents and token budgets — and bad workflows now replicate by the second. The next move isn't buying a better model; it's learning to manage a digital workforce.

With buy-online-pick-up-in-store and cross-store returns, a single transaction splits into multiple messy ledger lines. Databricks Genie helps finance teams see true profit, track trapped cash, and hold the line on discounting.

Compressing a ~54GB 27B model down to ~3.9–5.9GB lets it run locally on a phone, while retaining roughly 90% of average performance—here's how it works and where the trade-offs lie.

Hassabis wants the US to stand up a FINRA-style body that defines frontier models with a moving benchmark — a voluntary protocol now, a hard gate to market later.

Model capability has leapt forward in 24 months, but a composite reliability metric built by the SAGE lab has moved only 5–10 points.

Anthropic's 36-page playbook breaks down the graduation bar and common pitfalls for four startup stages, complete with matching Claude prompts you can copy straight in.

After the memory system launched, grocery checkout conversion rose about 24%, and automated evals scaled daily test volume from 1 human-reviewed case to 2,000+.

Retrieval becomes a sub-agent that plans and retries; evaluation upgrades from "does it run" to "did it do it right"

Across 3 models and 20 languages: English is the most cautious and in-depth, Russian the most exacting, Hindi the warmest, and Chinese sits closest to the global average

With no central brain in charge, nearly 200 simple smart cubes figure out what shape they've formed just by talking to their neighbors—and can even sense where to "regrow" after damage. The self-recognition part already works on physical bricks; damage localization and regeneration still happen mostly in simulation.

Companies pay for intelligence twice: once in model fees, and again in the proprietary know-how required to make the model genuinely useful.

The paper claims the code is open-sourced — but the repo turns out to be empty, without a single commit ever pushed.

Swapping models isn't just swapping an API: eval frameworks, tool parameters, caching, and reasoning traces — four invisible pitfalls, unpacked and fixed one by one

Based on 1.2M+ conversations across 600,000+ organizations: content creation ranks second at 16.4%, together accounting for nearly half of all usage.

Manufacturing, logistics, warehousing, and labor services have been stuck at single-digit margins for years — cutting coordination costs alone can multiply their profits.

576K samples, 16 models tested: 19.7% of AI-recommended packages are hallucinations, and 43% keep generating the same fake name.

The team dodged questions about benchmark gaming, faced backlash from longtime users over the desktop app merger, and admitted they're "still figuring it out."

From tacit knowledge to interaction bandwidth to model alignment — why AI's progress still can't do without humans.

A retrospective: since launch, Pinecone has run 75,000 sessions, served 600+ employees, and connected 37 internal systems via MCP Gateway.

OpenAI consolidates prompting tips scattered across its product pages into one framework — goal, context, output, constraints — plus dedicated Codex workflow examples.

All results are computer simulation predictions from a brain "digital twin" model, not yet validated with real human brain imaging.

No manual context-feeding required — the local Markdown wiki refreshes on a set schedule; a Slack connector is coming soon.

Local small models replace cloud AI calls — the numbers come from Google's internal testing; proxy models are currently limited to the ai.if function and still in preview

Pretrained on 2 billion hours of wearable data from 5 million people, a frozen encoder with just a linear head beats supervised baselines on 34 of 35 health tasks.

First-hand impressions from four scenarios: coding, writing, knowledge work, and agents

A ClaudeDevs deep dive untangles two knobs that both seem to promise a better answer: switching models swaps in a different frozen set of weights, while dialing up effort changes how willing it is to read more files, run more tests, and double-check before handing in the result.

Not a feature list — three engineering tracks turned at once: reasoning lifts intelligence, an efficiency stack cuts cost, native multimodality expands input.

Three levers — system prompt, tool descriptions, middleware — push the Deep Agents suite from a typical ~0.80 to 0.84, topping out at 0.86 against Opus's 0.87.

Former OpenAI safety lead surveys nearly 30 papers: from prompt tweaks to self-modifying code, DGM pushed coding ability from 20% to 50%.

Paper shows a 10.8% gain in personalization and 29.4% in reasoning, fully open-source under Apache 2.0

In advisor mode Fable 5 just gives advice; in orchestrator mode it delegates tasks — either way, the cheaper Sonnet 5 ends up doing most of the work

By fine-tuning only the single token where the doom loop begins, both models' loop rates drop to around 1%

Expanding from charging only AI crawlers to charging any caller, now in early access waitlist

It makes up less than 10% of the model — remove it and Claude can still talk, but its reasoning collapses to zero. Anthropic is already using it to catch fabricated data and spot when Claude senses it's being tested.

In a 151-student trial, short-answer questions moved scores more than multiple choice, while almost no one touched the AI help sidebar

No funding, no team — built purely to solve his own kid's problem. Clinics and schools started asking to use it anyway.

A live audience vote couldn't be tallied because the venue lights were too bright to count hands, but a companion survey found 95% of teams already use agents while 59% worry about mounting technical debt.

While traditional schools are still figuring out AI, Silicon Valley and Wall Street families are already voting with their wallets.

US developer employment among 22-to-25-year-olds has fallen 19% in three years, even as new GitHub sign-ups hit their fastest growth ever.

The paper is the first to run an agent through an entire RTL benchmark suite fully unattended—most tasks clear in two or three rounds, but the hardest one takes 82 iterations

A 7-month analysis of sessions from 235,000 users: verified experts succeed at nearly double the rate of novices — yet the top 10 professions differ by no more than 7 percentage points.

Create psychological tension first, then offer a first step too small to refuse — it works for writing, selling, and job hunting alike.

On June 12, U.S. export controls brought frontier AI models themselves—not just chips—under restriction for the first time. His answer: master multi-model orchestration.

Anthropic's Thariq argues the quality of your work with Claude Fable 5 hinges on how clearly you can name your own unknowns. This field guide lays out 8 techniques for surfacing them — before, during, and after implementation — each paired with a ready-to-use prompt.

Investor Chamath Palihapitiya: intelligence is getting cheap like phones, and expert judgment is now available to everyone — the real moat is encoding your proprietary experience into your own system, not renting the same generic AI as your competitors

She used the same pattern to build an email triage tool and a caregiving app for her dad — the method is repeatable, though the full prompts remain unpublished.

Four implementation principles, backed by real-world data from L'Oréal, Lyft, and Rakuten

MIT-licensed and model-agnostic — works with any OpenAI-compatible text model, though for now it only handles a single page view

现场demo：搜'露营'后，咖啡机网站文案产品全变户外主题；技术能落地，客户网站还没规模上线。

An instructor who has trained 30,000+ PMs breaks down two AI leverage ladders — from copy-paste to end-to-end delivery, and from web prototypes to production PRs.

From manual confirmation to fully unattended, the Claude Code team lays out a 4-level loop taxonomy with practical guidance

Trained on 50,000+ domestic AI chips and 35 trillion tokens; most benchmarks come from Meituan's own evaluation framework, and the weights aren't truly open for download yet

Now in open beta. A coordinator agent marshals a team of expert agents to do the work, with a reviewer agent at the end dedicated to catching errors in citations and numbers — compute gets outsourced to AI, but raw data never leaves your local machine.

Partnering with Thinking Machines, they fine-tuned an open-source model on expert-labeled data: 29.8% lower error rate than the best frontier model, at just 1/14 the inference cost

Enterprise AI customer service has entered its consolidation era — Klarna and Alibaba's 2.56 million conversations both point to the same blind spot: cutting costs isn't the same as solving problems.

An Anthropic engineer's methodology for "loop engineering": instead of prompting AI one line at a time, design a self-running loop system