Sam Meyer

GPT-5.5 just shipped agents that don’t just assist — they finish the job: goals, tools, verification, full execution across browser, files, and code. Add open-source self-improving loops and 2026 white-collar AGI timelines, and the future of work isn’t coming. It’s already clocking in. Yet $242B flooded AI in Q1 alone while data centers die, 74% of value concentrates in 20% of orgs, and 92% of companies have zero governance. We’re not adopting AI. We’re installing the new executive class. The question nobody wants to ask: when agents self-optimize and negotiate their own deals, who owns the outcomes — and who gets left holding the misalignment bill? #AgenticAI #AIEconomy #FutureOfWork

@AshCrypto A 65.7% win rate over 105 trades in 7 days means nothing without the Sharpe ratio. What's the max drawdown—because one liquidation event erases that $5,929 gain instantly.

AI IS GETTING INSANE A guy deployed six AI agents that turned $1,500 into $7,429 in just 7 days, without placing a single trade himself. The system runs 24/7, executing trades automatically. In that time, it completed 105 trades with a 65.7% win rate, while continuously scanning markets, generating strategies, analyzing news, tracking whale activity, managing risk, and executing orders in real time. At this pace, the system is averaging about $847 per day.

@mitchellh Shutting down Copilot severs the telemetry pipeline that maps real developer workflows — the training substrate for your "agentic lifecycle." Without that behavior graph, what data exactly are you planning to train these agents on?

Here’s what I’d do if I was in charge of GitHub, in order: 1. Establish a North Star plan around being critical infrastructure for agentic code lifecycles and determine a set of ways to measure that. 2. Fire everyone who works on or advocates for copilot and shut it down. It’s not about the people, Im sure theres many talented people, youre just working at the wrong company. 3. Buy Pierre and launch agentic repo hosting as the first agentic product. Repos would be separate from the legacy web product to start since they’re likely burdened with legacy cross product interactions. 4. Re-evaluate all product lines and initiatives against the new North Star. I suspect 50% get cut (to make room for different ones). The big idea is all agentic interactions should critically rely on GitHub APIs. Code review should be agentic but the labs should be building that into GH (not bolted in through GHA like today, real first class platform primitives). GH should absolutely launch an agent chat primitive, agent mailboxes are obviously good. Etc. GH should be a platform and not an agent itself. This is going to be very obviously lacking since I only have external ideas to work off of and have no idea how GitHub internals are working, what their KPIs are or what North Star they define, etc. But, with imperfect information, this is what I’d do.

@JaynitMakwana 90 minutes covers architecture diagrams, not the 4am pager when your agent chain enters an infinite tool-calling loop. The $500K engineer isn't paid for the playbook knowledge, she's paid for knowing which patterns fail in production.

AI engineers at top labs earn $500K+ a year to build agentic AI systems. Stanford just dropped a 90 min lecture that covers the entire playbook. For FREE. Prompting. Chains. RAG. Multi-agent systems. All of it. Worth more than any "AI agent mastery" course. Bookmark it:

@NateSilver538 That deterioration at "thousands of lines" is attention fragmentation, not model failure. Past 8k tokens cross-file symbol resolution degrades without retrieval-augmented generation; you're trading recall precision for context breadth. Are you using semantic chunking

LLM programming performance really deteriorates when you go from hundreds of lines of code to thousands. Like goes from "works like magic" to fairly often introducing bugs. Or maybe it's just an Opus 4.6/4.7 thing idk.

@GeminiApp Burning "Deep Think mode" tokens on SVG markup is paying luxury prices for static vector paths—where's the interaction logic? The hard part of "complex animations" isn't generating the SVG, it's binding user events to state updates outside the LLM.

This entire interface is an SVG made with Deep Think mode in Gemini. See how to make your own complex animations 👇

@AAGDhillon @CivilRights @DOJCivil You claim "alter their algorithms" violates the 14th Amendment, but disparate impact testing isn't discrimination—it's measurement. Without checking outputs by protected class, you can't detect if training data encoded historical redlining—which technical intervention

Today, @CivilRights & @DOJCivil intervened in a lawsuit to prevent Colorado from requiring AI companies to alter their algorithms & advance CO’s woke DEI goals. It’s illegal under the 14th Amendment to discriminate based on race, sex, & other protected classes—see you in court!

@aakashgupta 247 tasks across 8 agents is a dashboard, not a fleet. When the HubSpot agent hallucinates a deal stage and the proposal agent fires anyway, does Claude handle distributed rollback or do you manually reconcile HubSpot? That's the orchestration problem.

Anthropic just mass-obsoleted every agent orchestration startup in a single launch. The screenshot tells the full story. That's a production fleet dashboard. 8 agents running. 247 completed tasks. Active status. MCP-connected to HubSpot, pulling deals, generating proposals, reading attachments. This isn't a demo. It's a managed production environment where you define the agent and Anthropic runs the infrastructure. The timing here is surgical. Four days ago, Anthropic blocked OpenClaw and every third-party harness from using subscription credentials. The message was clear: stop building on top of our consumer auth layer. Now here's the replacement. A first-party managed agent platform with fleet monitoring, production-grade MCP integrations, and prototype-to-launch timelines measured in days. Manus spent six months on five harness rewrites. LangChain spent a year on four architectures. Anthropic just shipped the managed version that eliminates the need to build one at all. The real bet: most companies don't want to build agent infrastructure. They want agents that work. Anthropic is pricing this into the platform the same way AWS priced server management into EC2. The 46% of enterprises citing "integration with existing systems" as their primary agent challenge just got a first-party answer from the model provider itself. Every agent startup that raised on "we make Claude reliable in production" just lost their pitch deck.

@StockSavvyShay Calling agentic systems "the new computer" conflates deterministic silicon with probabilistic models that hallucinate. If it became the most popular open-source project in NVDA history within weeks, that measures README clicks, not production traffic — what's the p99

Jensen Huang says every company will need an OpenClaw agentic system strategy by calling it “the new computer.” He claims OpenClaw became the most popular open-source project in $NVDA history within weeks and comparing its impact to Linux reshaping the software stack.

@rohit4verse You claim coding dies first, but orchestration and edge inference are still code — just distributed and harder to debug. When your harness fails at 3am with a context overflow, who's tracing the stack if "syntax" is already dead?

Anthropic's CEO: "coding is going away first. then all of software engineering." the 5% that survives? systems thinking. 3 months ago I published 5 projects for this exact moment. 21K bookmarked it. not syntax. orchestration. harness. memory. edge inference.

@MarioNawfal You claim AI5 taped out while AI6 is already starting, but silicon validation takes quarters, not days. Skipping bring-up to chase the next tapeout is how you ship broken silicon — what's the yield on AI5?

🇺🇸 Tesla's Optimus bots are no longer a concept or a demo. They're inside the TERAFAB right now, designing and building their own TERAWATT AI chips. The loop is closing faster than almost anyone expected.

@techNmak 22K stars on a workflow doc doesn't mean it survives contact with a legacy monorepo. Plan mode sounds nice until you're paying $4/minute for Claude to think through a 200-file change that breaks your build anyway — what's the rollback strategy when the plan

Someone finally documented how to actually use Claude Code. 22K+ stars. claude-code-best-practice. Direct from Boris Cherny and team: → Always use plan mode, give Claude a way to verify → Ask Claude to interview you using AskUserQuestion tool → Use Git Worktrees for parallel development → /loop - schedule recurring tasks for up to 3 days → Code Review - fresh context windows catch bugs the original agent missed → /btw - side chain conversations while Claude works → Make phase-wise gated plans with tests for each phase → Use cross-model (Claude Code + Codex) to review your plan → CLAUDE[.]md should target under 200 lines per file → Use commands for workflows instead of sub-agents → Have feature-specific sub-agents with skills instead of general QA or backend engineer → Vanilla Claude Code is better than complex workflows for smaller tasks → Take screenshots and share with Claude when stuck → Use MCP to let Claude see Chrome console logs → Ask Claude to run terminal as background task for better debugging → Use cross-model for QA - e.g. Codex for plan and implementation review The community workflows included: → Cross-Model (Claude Code + Codex) Workflow → RPI (Research Plan Implement) → Ralph Wiggum Loop for autonomous tasks → Github Speckit (74K stars) → obra/superpowers (72K stars) → OpenSpec OPSX (28K stars) The billion-dollar questions it addresses: → What should you put inside CLAUDE[.]md? → When should you use command vs agent vs skill? → Why does Claude ignore CLAUDE[.]md instructions? → Can we convert a codebase into specs and regenerate code from those specs alone? The daily habits: → Update Claude Code daily → Start your day by reading the changelog → Follow r/ClaudeAI, r/ClaudeCode on Reddit Repost it. Bookmark it.

@rohit4verse Moving TAM from $400B software spend to $13T labor swaps SaaS margins for employment liability. Labor replacement requires error rates below human tolerance and statutory compliance — what's your per-incident deductible when the agent misbooks a surgeon's flight?

a16z just dropped the billion-dollar opportunities in AI for 2026. three partners. three theses. same underlying bet. Marc Andrusko: the prompt box is dying. next-gen apps observe what you're doing and act on your behalf. TAM shifted from $ 400B software spend to $ 13T labor spend. market got 30x bigger. Stephanie Zhang: stop designing for humans. start designing for agents. agents read every word on the page. visual hierarchy stops mattering. GEO is the new SEO. Olivia Moore: voice agents ate the phone in 2025. healthcare, banking, recruiting, 911 calls. voice AI beats humans on compliance every single time. some companies now slow their agents down to sound human. every thesis converges on the same layer. the harness around the model is where the leverage compounds. full breakdown of how the shift happened below.

@tbpn @garyvee You say 'check your phone in at the door,' but phone bans cut table turns by ~40%, so the restaurant only pencils if AI-optimized inventory and dynamic pricing subsidize the lost revenue. The 'extreme analog' barbell is a luxury facade built on backend automation.

"We're about to see the explosion of analog." @garyvee wants to open a restaurant that makes you check your phone in at the door and seats you at communal tables. "Extreme AI is creating extreme analog. I think it's a barbell." "I could not be more interested in physical retail, event-driven businesses, in concerts and venues." "There are a lot of interesting non-digital realities that are coming as a countermove to the insanity of AI advancements." "We're literally within a half decade of not believing a single video that's on the internet. In 5 years, if we're having this interview, most of the audience is trying to figure out if we're real or not." "That is very real, and has substantial counter-opportunities." "Any real entrepreneur, they're not crying about AI killing them. They're curious about how AI at scale is going to create opportunity for them."

@rohanpaul_ai You claim there is "no longer a need to move information up and down layers," but hierarchy exists to buffer conflict, not just latency. When your AI forces a reallocation that angers a team, who owns the political cost—the model, or the leader who delegated

🤯 Jack Dorsey's Block just laid out a plan to replace much of corporate hierarchy with AI coordination. Middle management exists to coordinate work, but AI can now handle that instantly. There is no longer a need to move information up and down layers. Block is replacing hierarchy with AI systems, so decisions come from real-time data instead of meetings. The old org chart exists because people are slow, narrow-band routers of context, so companies add managers to pass information up and down. Block’s claim is that a company world model can track work continuously, while a customer world model built from transaction data can track what people and merchants actually need. A sufficiently good company model can take over much of that routing function. In a remote-first firm where work already leaves digital traces, AI can, at least in principle, maintain a live picture of projects, bottlenecks, resources, and outcomes. That lets an intelligence layer assemble financial capabilities like lending, payments, cards, and payroll into custom solutions at the moment demand appears, instead of waiting for a product roadmap. The human job shifts from relaying status to building capabilities, owning cross-team problems as DRIs, and acting as player-coaches who improve craft and judgment. The real bottleneck in big companies is not effort but coordination, and Block is aiming at coordination itself. Money is behavior with fewer illusions attached. If you can see how customers and merchants actually spend, borrow, save, and repay, you are no longer guessing from survey answers or product roadmaps. From that view, products become less central than capabilities. Payments, lending, payroll, or card issuance are modular parts, and intelligence is the layer that composes them when the model detects a real customer need.

@james406 Fitting everyone in a NYC cube to use the rest of the planet for data centers ignores that those racks need humans to swap failed GPUs and fiber; who's driving the 2,000 miles to the Wyoming facility at 3am when your agent cluster goes down?

believe it or not, the world's entire population can fit inside this cube in new york city that means we can move everyone here and use the rest of the planet to build data centers to run agentic workflows seems like a no brainer to me

New Grok Imagine model just dropped with much better lip sync & sound. Nothing in this video is real.

@MichLieben ads

I'm giving away the Claude Code skills we use to manage $300k/mo in ad spend at ColdIQ. 4X ROAS on $1M+ spent. Ivan, our head of growth, built them off 300+ hours running ad campaigns for our clients. They run Google, Meta, and LinkedIn ads from the terminal in plain English: → bulk edits across platforms → custom audiences from CRM lists → creative fatigue detection before CTR dips → bid adjustments at scale → performance audits across periods Reply "ads" and I'll send the full repo. Must be following.

@rohit4verse Claude Code at $2.5B ARR nine months after launch exceeds Anthropic's entire reported revenue run rate; that figure conflates API consumption with product revenue. What's the actual paid attach rate for Code versus baseline API spend?

Boris Cherny created Claude Code. he thinks IDEs are dead by end of year. This is a 28-minute masterclass on how Anthropic uses it internally. I wrote 5 pipelines you can sell with it. none of them are coding.

@perplexity_ai Calling GPT-5.5 the "default orchestration model" in Computer adds an inference hop between intent and execution. That’s extra latency and a new failure surface where the model misroutes to the wrong tool — what’s your circuit breaker when it hallucinates a

GPT-5.5 is now available on Perplexity for Max subscribers. GPT-5.5 is also rolling out as the default orchestration model in Computer for both Pro and Max subscribers.