Swaminathan Lalgudi @lvSwami

@niyetsel My son should get good results in his IB 12th class 7 in maths and economics and 6 in physics

@Akrepburcusun 16

If you're adding a DESIGN.md to your repos, please align with the Google spec. While prose describing your design system is helpful, it's less help than following the actual spec with the frontmatter. github.com/google-labs-co…

AI service firms are commanding 30x multiples right now. Yes, thirty. That's why a16z, Sequoia, and YC are chasing services, not SaaS. Most agencies will see this and reach for the wrong move. They'll keep selling hours, bolt on AI, and cut headcount to pad the margin. But that's playing the small game. Here's why: 00:00 Why Services Beat SaaS 01:13 The $1 Software vs $6 Services Opportunity 02:52 Why Managed Growth Loops Matter 04:49 Agents, Loops, and Human Judgment 06:43 How Single Brain Powers AI Service Businesses 07:22 The Services-as-Software Manifesto 08:41 The New AI-Native Org Chart 10:13 Building Outcome-Based Offers 11:13 Final Thoughts

ericosiu @ericosiu

a month ago

x.com/i/article/2052…

16 42 453 523K 2K

You can build a deployed web app, run a full data analysis, and generate an investor pitch deck without writing Python or touching a chart library. I recorded a complete Claude Code masterclass proving it. 3 real projects, start to finish. Permission modes, sub-agents that fix their own mistakes, skills, MCPs, context management. All of it explained simply. Watch the full thing 👇

Vaibhav Sisinty @VaibhavSisinty

a week ago

x.com/i/article/2063…

10 20 143 26K 248

Google just open-sourced 13 official skills for AI agents. And they work with Claude Code, Cursor, and Copilot. Not just Gemini. 🤯 Read that again. Google built skills that make their competitors' tools better. One command to install: npx skills add google/skills Pick the ones you need. They load automatically. What's inside: → BigQuery, Cloud Run, Firebase, AlloyDB, Cloud SQL, GKE basics → Google Cloud onboarding, authentication, network observability recipes → Security, reliability, cost optimization from the Well-Architected Framework → Gemini API and Managed Agents Platform skills These aren't docs dumped into a prompt. They're compact expertise packs that agents load on-demand. No context bloat. No hallucinating old SDKs. Google didn't lock this behind Gemini. They made it work everywhere. That's the most un-Google thing Google has done in a while. github.com/google/skills

Every Agent Needs a Human Dan Shipper, CEO of Every, interviewed by Lenny Rachitsky (Lenny's Podcast) I post one executive summary daily of an interview I enjoyed and learnt from. I loved this interview that @lennysan did with @danshipper. Great insights around building and running a AI-native company for founders and operators. Summary: Dan Shipper runs what is probably the most AI-forward company in the wild, has doubled headcount in a year, and thinks the AI job apocalypse is wrong. His read after a year inside the frontier: agents do not work without a human running them, the work surface for knowledge work is collapsing into Codex and Claude Co-work, and the people who win are the ones who keep riding each new model into work they could not do before. SaaS is back. PMs and designers are back. Buy the stocks. 1. Automation Is a Lie. Every agent needs a human who cares about it. The minute you sever that connection, the agent stops being useful. Shipper has doubled Every's headcount in a year while running 6 AI products internally, and the math still works because someone has to watch the agent, add context, and fix it when it breaks. Treat "fully autonomous AI" as a marketing line. Staff for the human who keeps the agent working. 2. The Super Agent Won. Shipper used to believe in personal agents on every laptop. He flipped. The pattern that actually works is one company-wide agent with a forward-deployed engineer who keeps it healthy: Shopify has River, Ramp has theirs, Every has Claudie. Build for the central agent first. Push specialization down only as the models become more independent. 3. Codex Is the OS. The work surface for knowledge work is collapsing into Codex and Claude Co-work. Shipper has been at inbox zero for 10 days because Codex pulls every email, opens an in-app browser to research, and drafts replies while he monologues at it. The agent watches him, he watches the agent, and the document, the browser, and the model are all in one window. Anything you ship for knowledge workers in 2026 needs to make sense inside that one window. 4. CLIs Are Over. Most technical people at Every have moved off the CLI back into GUIs for Codex and Cloud Co-work. GUIs were invented for a reason and the benefits of the CLI era port cleanly into a window. Stop bragging that your product is terminal-native. Ship the GUI. 5. The SaaS Apocalypse Is Dumb. Shipper would buy SaaS stocks today. Every is the most AI-pilled company he knows and Every's SaaS spend is up year over year, because agents multiply the number of users a SaaS product serves. The right move for a SaaS founder is to design for humans and agents working on the same surface at the same time. The customer brings the tokens. You keep the margin. 6. The CEO Test. "Your company is only going to go as far as your CEO goes in AI." Shipper sees this with every senior leadership team he works with: CEOs who delegate AI exploration lose intuition for what their org can ship, and the company plateaus there. The CEO has to keep hands on Codex every week, even when it would be easier to hand off to a chief of staff. If you stopped playing with each new model drop, your team will too. 7. Yesterday's Competence, Commoditized. Models make yesterday's human competence cheap. The moment a new model lands, anyone can make a landing page or write a tweet or ship a feature at the median of yesterday's craft, so that work commoditizes overnight. Humans then take the frozen competence and push past it into something the model has not seen. The job is to stay on the edge of that gap, finding what the model has not yet absorbed. 8. Benchmarks Lie About Autonomy. Shipper built a "senior engineer benchmark" by handing models his own vibe-coded codebase and asking them to rewrite it. GPT 5.5 hit 62, up from 30 the prior generation, and headlines would say senior engineers are about to be replaced. The trick is that the prompt itself, telling the model to rip out the vibe-code and start from first principles, took weeks to craft, and a senior engineer would have known that on their own. The benchmark measures what we already learned to articulate. Humans do the part you cannot score yet. 9. PMs Eat the Future. A PM at Every named Marcus took a year off, got AI-pilled, learned Cursor and Claude Code, and now ships faster than anyone on the team while keeping his product instinct intact. A year ago Every could not have hired him for this role. Today he runs Spiral, Every's writing app, solo. PMs with a feel for users, taste for writing, and the discipline to drive a coding agent are the most valuable hire in tech right now. If your PMs have not flipped, that is the gap to close. 10. Designers Ship Now. Designers at Every are opening pull requests directly, because Claude Code makes the gap between mockup and merged interaction collapse. The interactions they design are the exact thing vibe-coding cannot do, which is why every "AI-built" site looks the same. A full-stack designer with strong taste is now a one-person feature team, and the design job market data has not caught up yet. Hire them while it has not. 11. Strategy Docs by Agent. Every ran its 2025 Q4 planning entirely through Notion agents. Each team talked to the agent about goals, metrics, and pushback against top-down strategy. Shipper got back planning docs better than most humans would have written on their own. The new etiquette: send AI-written docs all you want, but stand behind every line. If you cannot answer a question about something your agent wrote, you do not get to send it. 12. Ride the Models. The single instruction Shipper gives anyone worried about losing their job: ride the models. Every time a new model drops, point it at the thing you actually do, find what it cannot do yet, and come back next release. The frontier of AI is wherever a model meets a real human doing a real job. Every is in Brooklyn precisely because that distance from the lab is an advantage. Find your moment of joy with AI, share it, repeat.

It seems like @GaryMarcus was right: the AI revenue models are imploding

Business Insider @BusinessInsider

a week ago

Sam Altman said AI budgeting has recently become a "huge issue" for some companies, something that "never came up" earlier this year. bit.ly/4uxIGnv

151 217 1K 2.7M 588

x.com/i/grok/share/4…

x.com/i/article/2061…

As token budgets take on a larger part of operating expenses over time, model routing is the inevitable conclusion. This is also one of the biggest areas of differentiation for the applied AI layer over time. By understanding the different work patterns in your domain, and having strong evals for that domain, you’ll be able to cost/performance optimize effectively. We’re still likely at the point where most use-cases will need frontier performance for the foreseeable future; but soon you will be able to peel off individual use-cases and send them to lower cost models once the quality is sufficient for the task. Enterprises individually trying to figure this out themselves at scale will likely not be possible, so the products that can intelligently route these workflows to the right tier of model will be in a strong position to aggregate more demand.

Factory @FactoryAI

2 weeks ago

Introducing model routing to Factory. Factory Router picks the right model for every task, automatically. Maintain frontier performance while cutting costs by 25%.

199 215 2K 2.1M 2K

@niyetsel 2

We asked ourselves a question last year- can we go back to back? Here we are again 🏆🏆❤️❤️ @RCBTweets

keyboard_arrow_left Previous keyboard_arrow_right Next

Spotify's vp of engineering just broke down how the company builds with claude. Free, 27 minutes. 73% of their code is ai-assisted now. Their best engineers haven't hand-written code since december. Watch the full video 👇

Claude Opus 4.8 is available today on the web, the Claude Platform, and all major cloud platforms. Read more: anthropic.com/news/claude-op…

Chamath just delivered the clearest diagnosis of what is happening to enterprise software and the OpenAI Deployment Company is the most damning piece of evidence he could have picked. "The low end of the market is basically finished. There is no safe space." 90% of public SaaS stocks are down 30-80% from their 52 week highs, the median software stock is now negative over the last 3-6 months. Goldman Sachs reported that software forward P/E multiples fell from 35x to 20x, the lowest absolute level since 2014 and the smallest premium to the S&P 500 since 2010. The low end died first and fastest, because AI replaced it most directly. The small business tools, the lightweight project managers, the single function SaaS products that charged $49 a month per seat, those are being replaced by AI agents that do the same work as a workflow, not a product. You do not buy an AI powered tool, you describe what you need and it builds it and the seat based model that created the SaaS industry simply does not apply to that transaction. But Chamath's more interesting argument is about the high end and the tell he points to is perfect. OpenAI just raised $4 billion from 19 investors including TPG, Brookfield, Bain, and McKinsey to launch a consulting company and guaranteed those investors a 17.5% annual return to do it. On $4 billion in committed capital, that is roughly $700 million per year in guaranteed payouts, owed by a company that is projected to lose $14 billion in 2026. The goal of this venture is to compete directly with Deloitte, PwC, Ernst & Young, Andersen, and Cognizant. Think about what that structure reveals. OpenAI lost half of its enterprise LLM API market share from 50% to 25% between late 2023 and mid-2025, with Anthropic now leading at 32%. Its response was not to build a better model but rather to raise $4 billion, offer guaranteed PE-tier returns and hire embedded engineers to physically sit inside client organizations and make AI actually work in production. The reason, as Chamath identified, is that the high end of the market is not easy. "It's not like boop boop boop, put in a prompt and beep bap boop, it all works," he said and the data confirms exactly that. 88% of organizations running AI agents reported a security incident in the past year, 42% of C-suite executives say AI adoption is creating internal organizational conflict. The average enterprise AI consulting implementation costs $228,000 in year one versus $77,000 for platform-based approaches and most still stall before reaching production. Anthropic immediately matched OpenAI with a competing $1.5 billion consulting venture backed by Blackstone, Goldman Sachs, and Hellman & Friedman bringing the combined spend by the two leading AI labs on human powered enterprise deployment to $5.5 billion in a single month Chamath's read is that the high end, the large enterprise platforms like Salesforce with proprietary data flywheels, Palantir with its FDE model already proven at scale, Oracle with vertical specific data moats will survive and consolidate. The mid-market point solutions, the single function tools, the lightweight enterprise apps without defensible data assets, those are on the conveyor belt. The AI industry is not just disrupting the companies that use software but rather disrupting the companies that sell it.

My favorite way of interacting with Claude Code is to have it generate static HTML files as outputs (reports, explorations, code structure, mockups etc.) I wanted to iterate on the file by commenting in browser and having Claude update the output live. So, I built this Claude Skill👇 How it works: - Install Claude Code skill (ask it to clone repo) - Build an HTML page for anything (e.g. research coding agents and generate HTML report) - Ask it to make the page interactive That's it. CC will launch a localhost server and allow you to then leave comments on the page itself and once it updates, will give you a tour of changes. It's like Google Docs kind of comments/iteration but for HTML pages.

Todo el mundo cree que la caída del cabello es genética e inevitable. Pero eso es mentira. Es tu cuerpo sacrificando cabello para sobrevivir al estrés. Aquí está la verdadera causa (y cómo revertirla):

We’ve automated every single thing we can @every with AI agents. And yet there’s way more human work to do than ever. We’ve gone from 4 -> 30 human employees since GPT-3. I wrote a report on the structural reasons: how AI makes expert competence cheap, why that drives up demand for experts, and why the dynamic only intensifies as we approach AGI. After Automation: every.to/p/after-automa…

If I ran TCS today, I'd do three things this weekend: One — make the top 1-5% talented implementation engineer a Forward Deployed Engineer. This will be the biggest upgrade in their title. Two — buy a few AI tooling startups. You can't sell AI services in 2026 with case studies that say "digital transformation" and without a deep understanding of AI tooling. Three — fly to SF and announce that we have 1000+ FDEs ready with deep AI expertise. The Indian IT services industry needs to win the AI implementation narrative.