This isn't a hire for someone who can write clever prompts. It's a search for people who can get model capabilities, customer workflows, and safety boundaries all the way into production systems.
A Bluetooth headphone glitch reveals how the site runs inaudible audio, harvests device details, and builds a fingerprint in the background.
Capital used to turn into engineers and a two-year wait. With AI, $10 more directly becomes training, tokens, usage, or targeted capabilities. Profit still isn't guaranteed, but the dynamics are rewriting private markets, model competition, marketing, and product value.
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.
Manual investigation often took more than an hour. Claude Tag now stays in Slack 24/7 and delivers its first evidence-backed analysis in a median of 14 minutes.
There is no single winning formula in nature or business. Steph Ango has cataloged 80 ways to gain an edge; I've translated each one into plain-language mechanics, scenarios, and examples.
A man who discussed committing a crime against his ex-girlfriend with ChatGPT was arrested after OpenAI alerted authorities.
From IGTV's failure and ChatGPT's empty text box to the very different design rhythms of Groupon, Instagram, and OpenAI, Ian Silber clarifies a harder question: when anyone can ship a product quickly, what exactly should a designer be evaluating?
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.
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.
For the first time, a major frontier lab has openly handed offensive cyber capabilities to verified "trusted defenders" — and put them to use finding real vulnerabilities in Chrome.
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.
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.
He starts with a loop every software developer will recognize, then explains why that loop can now be broken at its root.
When an agent wants to merge code before a human approves it, the system pretends it did—designing every layer around the certainty of AI mistakes.
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.
We went through more than 40,000 generation records line by line to reverse-engineer the entire pipeline: a shared 12-line technical foundation for every shot, a three-view asset sheet, a focal length reference table, and a set of prohibitions born from model failures. All of it is ready to copy.
After losing his temper at his AI coworker, Steve Yegge turned 'how to treat an agent' into a copyable architectural spec.
A fast model alone isn't enough to make AI speak without stuttering—every link in the chain, from the moment you hit the button, has to hold.
The founder personally installed the product for the first 100 customers, whose biggest payoff was recovering revenue that had been slipping through the cracks.
The report also unpacks three widely misunderstood metrics: the declining token curve, six months of backlog, and 5% of entry-level roles.
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.
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.
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.
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.
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.
It takes a different route than Doubao's or GPT-Live's end-to-end full-duplex systems — the pacing can't quite match theirs, but every piece of the pipeline is open source, swappable, and runs on your own hardware.
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.
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.
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.
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.
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.
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"
Companies pay for intelligence twice: once in model fees, and again in the proprietary know-how required to make the model genuinely useful.
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
Manufacturing, logistics, warehousing, and labor services have been stuck at single-digit margins for years — cutting coordination costs alone can multiply their profits.
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.
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.
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
16 insiders recount the journey from wrestling with diffs to a two-week sprint launch — and how engineers stopped writing code by hand
Claude Code team's Thariq Shihipar at a conference talk: the bottleneck for new models is no longer the model itself, but whether you can articulate your own unknowns
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.
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.
Multi-agent systems can boost performance by 90.2%, but they also cost 10-15x more in tokens. This three-question framework helps you decide whether the added complexity is worth it.
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.
Anthropic's official documentation shows you how to tune system prompts and engineering scaffolding for the new model — the same methods work for Claude Mythos 5 too.
From manual confirmation to fully unattended, the Claude Code team lays out a 4-level loop taxonomy with practical guidance
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.
Brockman confirms OpenAI is developing multiple hardware devices; Agent has only ~20 million users, while ChatGPT is nearing 1 billion
An Anthropic engineer's methodology for "loop engineering": instead of prompting AI one line at a time, design a self-running loop system
Every runs five products with a one-person team — the core habit is one extra step after every feature ships: save the fix back into the system so AI automatically avoids the same trap next time.