Library / Deep Dive
Deep Dive
160 explainers · newest first
160 explainers · newest first
Government portals, commercial websites, and hosting platforms initiate emergency maintenance over PHP2Shell as Sanae Takaichi urges rapid patching. AI makes complex vulnerability verification easier, but defense still requires deployment across the entire service chain.
The Claude Code team integrates review throughout the entire development process, letting machines catch flaws, having humans gate changes based on risk, and establishing convergence rules for AI reviewing AI.
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.
A complete breakdown of Desk Racer's production workflow—building a playable tabletop racer with Gemini, Blender MCP, and Three.js, then recording a demo video. Includes the original video with Chinese/English subtitles and complete English follow-along prompt templates.
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.
Follow Florence's Motion Lab: start with a quick title animation, then learn how to craft earbuds, liquid chrome, and character animations.
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.
On one hand, rapid growth still fell short of previous expectations; on the other, AI is driving hiring to place greater value on creativity and learning agility. This DealBook edition also covers Starbucks' M&A, mortgages, taxes, and satellite communications.
Tracing the path across DIL descriptions, server compilation, and client execution to unpack streaming display, state updates, data injection, and AppBlock.
A guide to the key changes across research, industry, politics, safety and next-year predictions in this 244-page annual report.
Six design rules from Anthropic’s automated brief: define tasks and schedules, filter updates, confirm delivery, and recover from failures with clear permissions and memory.
Start with one small browser task, then configure your environment, copy verification commands, and review changes. Use parallel tasks, local handoff, and automation when you need them.
Lauren Tan’s experience shows a shift in responsibility: turning tasks that repeatedly require human intervention into capabilities AI Agents can use on their own.
Following official examples of PDF form filling and data querying, this guide clarifies how a Skill evolves from real task failures into a reusable methodology.
From plain text to interactive web pages and custom explainer videos, an in-depth look at turning AI outputs into understandable, verifiable results.
From hand-drawn animations to product launch videos and an island fishing game, understand how ten community projects are built, where to start reusing them, and their practical prerequisites.
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.
From start and end frames and asset tags to timecodes and localized edits, this breakdown explains how to use the official guide step by step, complete with Chinese prompts and original video examples.
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.
Enter the canvas, reuse references, edit details, choose styles, write prompts, make images and videos, and check deliverables across 19 project types.
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.
Anthropic added build-eval and hillclimb to the claude-api skill: one builds and grades the eval, the other changes one thing per round and uses held-out tasks to catch overfitting. In a support example, cost fell to about a fifth; the skill itself went from 66% to about 88%.
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.
Like a newly hired virtual coworker, a Team Bot receives a handbook, tools, restricted permissions, and working memory so it can move tasks across apps and carry team decisions and corrections into the next round of work.
Claude can help build 3D scenes, remove unwanted objects, composite shots, and grade footage while leaving projects you can keep editing. What can these skills handle, and what do you need to prepare?
Understand what effort changes, examine three development experiments, then choose settings across requirements interviews, rapid implementation, human review, and thorough verification.
X user NotinReality handed the lyrics and audio to Claude Opus 5.5, which wrote JavaScript to paint a 156-second hand-drawn watercolor music video frame by frame, without any image or video generation model. This piece breaks down the difference between the two rounds, how the model split the work into chapters and ran them in parallel, and then walks you step by step through making your own animation with ClaudeAnimationBase, the template the creator open-sourced.
Anthropic's developer guide organized around "what problem are you facing": migrating from Opus 5, adjusting effort, max_tokens, disabled thinking, unattended agents, progress updates, refusals, paste injection, vision, and frontend generation—complete with copy-pasteable official prompt snippets.
Calculate the cost of a task using list API pricing, then optimize spending through testing, effort levels, model selection, caching, and /usage—complete with two reusable templates.
Gagan Biyani announces the founding of Horowitz Andreessen Academy: restructuring post-high school learning around self-directed projects, short courses, and industry co-ops, backed by Silicon Valley tools, teams, and peer networks to help young people turn ideas into real work. What is this training model trying to change, and what does it still need to prove?
A practical sequence for setting a finish line, running long Claude Code tasks, reviewing the work, and handling model switches and speed settings.
SpaceXAI integrated Grok Bot into ticketing, engineering, and observability systems, spanning preliminary investigations, refund execution, and closing the loop with product teams. The core of this playbook lies in task triaging, gradual permission delegation, and continuous improvement.
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.
As AI makes building features increasingly cheap, what product teams genuinely lack is shared mission, clear decision rights, and people who can simultaneously embody Confidence × Humility, Process × Chaos, and Long-Term Patience × Daily Action.
Mati Staniszewski breaks down ElevenLabs' product strategy, team management, and enterprise growth playbook, with a deep dive into the FDE approach borrowed from Palantir: deploying engineers directly to customer sites, then feeding delivery learnings back into the core product.
Jev, released by TypeSafe, doesn't chat or write text—it only answers multiple-choice questions you provide and tells you how confident it is. Getting started takes just four steps, delegating installation and coding entirely to Codex or Claude Code. Through official and community real-world cases, demo videos, and our own hands-on tests, we clarify where it can be used and where it might fail.
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.
A complete catalog of 43 workflows and skills, a week-by-week automation cadence, 37 existing connectors, and five business case studies—with a clear note that Anthropic has not published the full list of 27 newly added integrations.
Codex has shifted the basic unit of work from line-by-line coding to goal delegation, pushing bottlenecks downstream to CI, review, deployment, and operations. This explainer examines the evidence, human checkpoints, and future automation directions of OpenAI’s nine-step software factory.
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.
Eighty percent of companies have yet to see AI impact their bottom line. The issue is not just model capabilities, but that enterprises are still jamming agents into legacy processes: workflows, governance, leadership approaches, and talent development must all be rebuilt together.
A step-by-step guide to building the full loop: start with one domain Bot, delegate to Cloud Agents, validate Proof, patrol Notion, merge by risk tier, learn through Jenny, and respond to P0 incidents.
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.
True agent customization is not about chasing the strongest model, but engineering a harness tailored to your business task through minimal primitives and composable middleware.
From item-by-item authorization for Plain, Stripe, and Databricks SQL, to copy-ready instructions, deliverables acceptance, stop conditions, and Routine setups across five support workflows.
What Grok Bot is, how to define a Bot with three fields, how to trigger tasks via chat, routines, and other Bots, and how to manage the permission risks of a shared computer.
As models grow more capable, the repository rules once written to scaffold weaker predecessors are now actively holding them back. This guide turns OpenAI Developers' recommendations into four actionable refactors: narrowing skill descriptions, turning AGENTS.md from mandatory reading into an on-demand index, granting explicit authority in safe environments, and encoding definitions of done directly into task prompts—complete with verification checks for every step.
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.'
Designer Taras Bakusevych synthesized research across human–computer interaction (HCI), mixed-initiative systems, and automated trust to propose 39 AI interface design principles. This explainer reorganizes them according to Microsoft's product lifecycle stages while verifying the original evidence and qualifications point by point.
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.
Using AI primarily in 1-on-1 chats keeps work trapped inside isolated dialog boxes. Moving from solo chatting to cross-role team collaboration requires understanding the genuine gains of human-AI teaming, engineering goals, and permission boundaries.
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.
Anshu Chimala turned the exploration, critique, and pruning methods he developed while leading software engineering and design teams at Apple into a practical guide that AI agents can execute.
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.
A full engineering guide to Uber's software factory: model and tool gateways, managed DevPods, a skill marketplace, context graph, end-to-end agentic SDLC, maintenance loops, and cost engineering.
Chats answer a question, agents complete a task, but the next phase of AI keeps context across time, executes continuously, and joins multi-person workflows. OpenAI product lead Tara Seshan explains what that working relationship demands—and how human responsibility shifts upward.
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.
Starting from the gap between in-repo and out-of-repo context, this piece breaks down how design.md, shared brand CSS, and an evaluation loop work together—and how a typical team can build its own design feedback system in six steps.
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.
Behind a night that auto-merged 20 PRs lie more than 600 refactoring PRs. Lauren Tan shows how architecture, CI, verification skills, and tiered delegation come together to form a team of agents that keeps working.
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.
Codex lead Tibo lays out a roadmap that matters more than model upgrades: fold Skills, memory, sub-agents, tools, and compute management back into the system, and let the same AI actively adapt to each person.
Writing code is no longer the main bottleneck. Requirements, testing, review, and operations are what slow delivery down now. Anthropic's approach leans on structured artifacts to keep agents moving in sequence, with the system validating first and humans stepping in only at the critical gates.
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.