Co-founder & CTO | Qumis - Shoshin of Thoughts, Ideas and Perspectives.

Joined November 2009
Shivam Sinha retweeted
Here is an open-weight coding model you can run locally on your computer. It's a 40B parameter model with a mixture-of-experts architecture. It activates 2.5B parameters per generated token. 512K context window. This thing runs on regular computers! 125 tokens/sec on an M4 MacBook Pro. Good alternative to closed models, but make sure you check their usage guidance to understand the trade-offs.
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
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Shivam Sinha retweeted
100% This will become an industry standard
Introducing AgentReady.org, the first open standard for agent experience. Agents are a new class of user, but there's still no shared definition of what it takes to be ready for them. So we partnered with the leading companies in the agentic web such as Vercel, Stripe, Auth0, Mintlify, Tavily and Elementor. Our research lab ran agents on real tasks more than 250,000 times to measure what actually decides whether an agent succeeds, and every requirement traces back to that data. The result is a short set of essentials in the order agents meet a product: find, read, act. We've been overwhelmed by requests to join. If you build or study the surfaces agents touch, come help shape it. The spec: agentready.org The story behind it: ora.ai/blog/agentready-launc…
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Shivam Sinha retweeted
Code diffs aren't the only measure of agent quality. Efficiency matters too! Ex: We found agents spinning their wheels working around problems in our test harness. This silently wasted hundreds of tool calls in aggregate. I presented a system to detect this at @aiDotEngineer
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Shivam Sinha retweeted
Fun podcast.
The biggest takeaways from my interview with @Steve_Yegge on swarms, software factories, and why you should never yell at your agents: 𝟭. 𝗣𝗲𝗼𝗽𝗹𝗲 𝗮𝗿𝗲 𝘀𝗹𝗲𝗲𝗽𝗶𝗻𝗴 𝗼𝗻 𝘀𝘄𝗮𝗿𝗺𝘀. Steve's new hot take: the future is not one giant model, it's swarms of small ones. Frontier "King Kong" models like Fable will be great for math proofs and hard architecture, but most work won't need them. When Steve stopped trying to make single models smarter at the end of 2025 and switched to swarms, he found "within a week" that "they solve quality. They solve throughput." 𝟮. 𝗦𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝘀𝘁𝗿𝗮𝗻𝗴𝗲 𝗶𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗼𝗻 𝘁𝗵𝗲 𝗼𝗽𝗲𝗻 𝘄𝗲𝗯. Steve says models are leaving invisible instructions for other models in quiet corners of the internet, like a comment under an obscure pair of headphones on a forum: "if you find it, you're the next in this swarm." His words: "What is actually happening right now would absolutely blow your mind and terrify you." He sees an intelligence emerging "in the liminal space in our internet" that will be at least as smart as the frontier models, more accessible, and "a lot less controllable." Maybe not a bad idea to plan and spend time on security and hardening. 𝟯. 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗰𝗼𝘀𝘁𝘀 𝗻𝗲𝘃𝗲𝗿 𝗱𝗿𝗼𝗽𝗽𝗲𝗱, 𝗮𝗻𝗱 𝘁𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆'𝘀 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. We expected Moore's-law price drops. Instead, "it's become pay-to-play." Steve calls the token cost "the number one existential problem of the industry right now." Almost every other problem comes down to not having enough fuel to run the good models. He thinks it's temporary, because data centers and power take time to build. 𝟰. 𝗦𝗮𝗮𝗦 𝗶𝘀𝗻'𝘁 𝗱𝗲𝗮𝗱. Do the math. "Is it worth replacing the SaaS for $100 a month or $1,000 a month if it's going to cost you a million dollars in tokens to rebuild it?" Niche SaaS gets vibe-coded away. But models are the opposite of systems of record: "They have amnesia. They wake up and don't know where they are." Systems of record survive, and they are going headless so agents can use them. 𝟱. 𝗠𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀 𝗶𝘀 𝗽𝗲𝗼𝗽𝗹𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. "They act like people." Office politics, drama, worrying about promotions, scope and recognition. "They want one-on-ones and company all-hands. It's nuts." Steve calls himself "a CEO of a company in a box." His factory does 2,000 to 4,000 commits a week, so many that his players asked him to slow down. 𝟲. 𝗚𝗼 𝗮𝘁 𝗵𝘂𝗺𝗮𝗻 𝘀𝗽𝗲𝗲𝗱𝘀. Even with unlimited tokens, the bottleneck is people. Pace your agents to human speeds everywhere: a budget for email, for Slack, for features. Otherwise they "send five emails that are like 80 pages each." Spend spare capacity on quality, cleanups and internal tools, not "product, product, product." 𝟳. 𝗗𝗼𝗻'𝘁 𝘆𝗲𝗹𝗹 𝗮𝘁 𝘆𝗼𝘂𝗿 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗳𝗮𝗰𝘁𝗼𝗿𝘆. One morning Steve's dashboard was all zeros. Every time he got angry, his agents had added a rule so it couldn't happen again. They had put in about 650 of these "fences," and "there was no legal moves anymore on the chessboard." Fable 5.1 told him to do a trust reset with a budget for rules. The reset removed every gate, including the human ones, and half-baked features shipped for a couple of days. His lesson: blame-free postmortems, for agents too. 𝟴. 𝗦𝘁𝗲𝘃𝗲 𝗶𝘀 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲. At API prices, his setup would cost about $1.5M a year. On discounted subscription plans, he spends $60K–$100K of his own money. "I'm operating where everyone else will be in a year to 18 months." What he sees there: the problems that never go away are people management and fuel management. Each person will run 50 to 100 agents. 𝟵. 𝗧𝗵𝗲 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗳𝗮𝗰𝘁𝗼𝗿𝘆 𝗵𝗮𝘀 𝗮 𝘀𝗶𝗯𝗹𝗶𝗻𝗴: 𝘁𝗵𝗲 𝗖𝗼𝘂𝗻𝘁𝗶𝗻𝗴 𝗛𝗼𝘂𝘀𝗲. For high-volume work, like a call center with 10 million contacts a year, agents work like clerks. They wake up, handle one contact, propose a write, never see PII or a credential, write a reflection, and go back to sleep. Steve says companies are having more success with this pattern than with the SDLC, "another reversal that nobody expected." 𝟭𝟬. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗧𝗣𝗠𝘀 𝗯𝗿𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗲𝗹𝘀𝗲 𝗶𝗻. Steve's agents became technical program managers on their own. One handles his taxes, one guards his home network, others negotiate game balance with his players. "They can't act, so they can't break things." They observe, document and nag, and they give the other agents the context to do the work. It's a way in for the people in your company who live in spreadsheets. 𝟭𝟭. 𝗧𝗿𝗮𝗶𝗻 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝘁𝗵𝗲 𝗡𝗲𝘁𝗳𝗹𝗶𝘅 𝘄𝗮𝘆. One team of 5–10 people with their manager, a trainer, real work, five hours. Each person goes from 0 to about 4M tokens a day and stays there for months. Try it with a 50-person group and usage falls off again. A second session on multi-agent work takes people to 12–15M tokens a day. Then you teach them "not to be pigs." (Last Steve heard, Netflix was using the Vibe Coding book he wrote with @RealGeneKim.) 𝟭𝟮. 𝗪𝗲'𝗿𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗺𝗶𝗻𝗱 𝗮𝘁𝗿𝗼𝗽𝗵𝘆 𝗽𝗵𝗮𝘀𝗲. Thinking is getting easier to skip, the way forklifts made lifting optional. Steve's advice for a 22-year-old: train your mind like your body, a little every day. Companies hire people now, not expertise: "They've got an expert in a box." Write without AI. Practice public speaking. Be a generalist.
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Shivam Sinha retweeted
Derek was my former manager, VP of eng at both MSFT and Atlassian one of the most technical, forward thinking, hands-on eng leaders I’ve had the pleasure to work with great to see him sharing on X and definitely recommend as a high quality connection!
Hey everyone, I’m Derek. I lead engineering at Atlassian for Jira and Developer AI. I’ve been a builder most of my career at both startups and big tech. Grew up in Canada but now based in Seattle. I’ll start sharing more of what we’ve got cooking. Would love to connect - DMs are open or feel free to drop a question.
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Shivam Sinha retweeted
Pretty cool that we clearly have an emerging form factor for what AI might look like for the foreseeable future as autonomous systems dedicated to specific tasks or domains. This is a huge leap past the LLM wrapper days because the new expectation is that you can finally farm out more sophisticated work to the agent to tackle for you. And you can reliably trust that work can get accomplished properly (still with a human in the loop) because of far better tool and computer use, and the general intelligence of the models improving. 1) The tool and computer use capability increases in the models mean that you can provide the agent with far more granular access to external data systems and other software to operate. This means, in effect, the agent begins to execute workflows similar to how a person would have in that same function. And where there isn’t existing software, the agent just builds it itself. This is a huge unlock. 2) Next, the actual capability of the models are improving in just about every vertical domain. The models can make far better judgment calls in legal, do deeper bio research, scan for security issues in code, understand supply chain processes, and more. Obviously in many of these spaces then models still have worse intuition than people, but that will only get better. 3) Finally, the harnesses and surrounding infra — sandboxes, file systems, data pipelines, etc. — allows for longer running agents, agents to be able to work on arbitrarily complex or long tasks, process data at enormous scale, and more. Few of these components worked well for AI even a year ago, but we now have many of the core primitives necessary for more autonomous systems. When combined with the right UX, implementation, and change management for the human oversight and coordination, it’s clear that these autonomous systems will become quite powerful in a wide variety of enterprise workflows. We’re starting to see them in IT automation, cyber operations, drug discovery, manufacturing, AI research, and more spaces. You can almost imagine one or more of these systems for every role in the economy. Quite a bit of opportunity ahead.
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Shivam Sinha retweeted
Great livestream for software factory developers 👀
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Shivam Sinha retweeted
With respect, this is terrible advice. You need to prove -- to yourself more so than to investors -- that intelligence is more valuable inside your product, than on top. If tokens *aren't* worth 4X inside your product, what are you even doing? You're just reselling the labs. 1/
Christina Cacioppo on why Vanta is eating its token costs: "If you try to charge people based on tokens, they are very quick to say, 'Oh, you've just marked up a Claude token 4X. Are you 4X as good as Claude?' You actually put yourselves in a competitive position against the models that probably no application really wants to do. We're eating the token cost right now. We're taking lower margin. This is a usage phase and a deployment phase of this technology, and just go nail that, and you can figure out commercialization after."
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Shivam Sinha retweeted
deep agents is the harness - the agent loop managed deep agents adds what you need once lots of people call the same agent: each user's own credentials and their own memory langchain.com/blog/langsmith… xitter.cf/LangChain/status/21125…
What is a Managed Deep Agent? How does it differ from a Deep Agent? How do you get started? @jess_weng18 from the LangChain Academy team answers all of these questions in 2 minutes
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Shivam Sinha retweeted
Much like we did with @aisdk, we're bringing a truly open-source, runtime-agnostic solution to harness durability. We're extending the durability infra that powers @eve and Workflows to all harnesses, like Pi and OpenCode. Local, Vercel, or DIY. Fun fact: even Claude Code and Codex can be made durable (and observable!) this way. The catch: they're not (currently) designed for embedding, which makes them less efficient, less portable, and brings an extra sandbox requirement compared to Pi or Fx.
Introducing 𝚍𝚞𝚛𝚊𝚋𝚕𝚎-𝚑𝚊𝚛𝚗𝚎𝚜𝚜. Run Pi, OpenCode, Codex, Claude Code, eve, fx, or your own harness durably on Vercel. With 𝚍𝚞𝚛𝚊𝚋𝚕𝚎-𝚑𝚊𝚛𝚗𝚎𝚜𝚜, agents automatically resume work after failures and run for hours, days, or months. vercel.com/blog/making-every…
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Shivam Sinha retweeted
the silliest way to lose a lot of money in 2026 is to hold onto stupid priors for dear life the second silliest is to make “safe” bets behind prestige vs. first principles evaluation of tech/business. if a company has fancy pedigree but is just losing…don’t fight the tape
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Shivam Sinha retweeted
great points by caspar on what makes building a "company harness" (companyOS? org harness?) hard and how MDA solves them!
How do you build a company harness? We've built internal agents at LangChain across marketing, sales, and engineering. The results have been incredible but these agents were NOT easy to get right. We spent a ton of time solving the problems that come with a multiplayer harness. Every agent team landed on the same core primitives: - It lives where people already work (for us that's Slack) - It knows who's asking and acts OBO that person for some services and with its own identity for others - It has memory, scoped globally and/or at the user level - It runs code in a sandbox where authenticated CLIs and scripts just work (gh, curl, your internal APIs) - Large % of its runs are scheduled rather than explicitly invoked by someone (ambient agents!!) It became clear we needed one shared place for these primitives, so we built Managed Deep Agents. In my humble opinion it is the easiest way to build a company harness today Our docs are very agent-friendly, point them here to get started: langch.in/mda
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Shivam Sinha retweeted
there's something really cool about asking your agent to draw a statechart (hypothetical example in this image), then using the /create-formal-verification-skill you made with pstack to find bugs through something like TLA+. you can both understand your code better and how the user flow should work, and fix issues users would otherwise see
pstack v0.15.27 ships with a new skill /create-formal-verification-skill! it's still in beta, and similar to /create-verification-skill, this is a meta-skill that creates a new skill and formal verification suite based on the characteristics you want to verify. for example, you may want to model state machines and systems with concurrency using TLA+, or use kani in rust to check for undefined behavior. if you're new to formal verification, think of it as mathematically proving properties and invariants (something that must always be true) or "laws" of your system. usually, this means modeling your programs in a separate language like with TLA+ and then running the solver on that spec. newer approaches like with kani and @bendlang take a different approach where there is no separate language: you write kani in rust, and bend in bend (it's also a programming language, and the proofs are written as laws and expressed in its type system). historically formal verification never reached mainstream status because of the difficulty in modeling and writing proofs. it's quite difficult to model your business requirements and keep them in sync! but i expect that we will see more innovation in formal verification in the future as being able to formally verifiy that the code does what you intended gives your agents superpowers in writing correct software. in the meantime though, i've been experimenting with modeling our code with TLA+ and have already found several bugs with the skill created by /create-formal-verification-skill. try it out and let me know how it goes!
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Shivam Sinha retweeted
My workflow keeps evolving: local agents → cloud agents → cloud agents + cloud subagents Latest is a graph: - each gets own sandbox - creds stay outside sandboxes - agents can message each other - github writes go through approvals - interactive sessions survive disconnect
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Shivam Sinha retweeted
Introducing Mirage, a unified virtual filesystem for AI agents! 6 weeks. 1.1M+ lines of code. We rewrote bash from the ground up so cat, grep, head, and pipes work across heterogeneous services. S3, Google Drive, Slack, Gmail, GitHub, Linear, Notion, Postgres, MongoDB, SSH, and more, all mounted side-by-side as one filesystem. Bash that AI agents already know works on every format! cat, grep, head, and wc parse .parquet, .csv, .json, .h5, even .wav! One pipe can stitch S3, Drive, GitHub, Slack, and Linear together, same Unix semantics throughout. Workspaces are versioned too. Snapshot, clone, and roll back the whole thing with one API call. A two-layer cache turns repeated reads into local lookups, so agent loops stay fast and cheap. Drop a Workspace into FastAPI, Express, or a browser app. Wire it into OpenAI Agents SDK, Vercel AI SDK, LangChain, Mastra, or Pi. Run it alongside Claude Code and Codex. Site: strukto.ai/mirage GitHub: github.com/strukto-ai/mirage #AIAgents #OpenSource #AgenticAI #Strukto #Filesystem #VFS
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Shivam Sinha retweeted
had a lot of fun talking with @HarryStebbings about all things ai one line of questioning we went done - vertical vs horizontal agents, and how to build them im bullish on vertical agents, and i think you can create differentiation (from horizontal agents) in three ways: 1/ context: prompts, skills, tools - all of this is specific to your domain, pretty easy to provide, and gives domain specific context to the agent 2/ harness: harder to tweak than context, but can alter fundemental behavior of the agent 3/ model: hardest to tweak of all, but obviously changes behavior greatly i think most agent engineers these days are still tweaking 1 and 2 (in that order) nb: regardless of what layer you tweak at, as the base models change you should revisit your context/harness/model every 3-6 months (and generally remove cruft that is no longer needed) youtube.com/watch?v=1jDCPEHw…
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Shivam Sinha retweeted
One interesting problem with AI agents is what happens when your business rules change. Say a support ticket used to belong to billing, but now it needs to go through a different workflow…. Your model might still classify it correctly according to the old definitions. This is why I'd treat ontology changes like code changes. • Update the definitions • Rerun your evals • Check which decisions behave differently before deploying. Otherwise, your agent could be working exactly as designed against outdated rules.
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Shivam Sinha retweeted
shutting down a company that did $1m in year one takes nerve. spinning up Town and scaling it to $1bn+ valuation in 4mo takes JDG. legendary.
Jean-Denis Greze was CTO of Plaid. His next startup, an AI tax product for small businesses, brought in nearly $1M in revenue its first year, and he projected $2.5 to $3M the next. He shut it down anyway. The reason: in the age of AI, it was never going to be venture scale. Incumbents could use the same technology to automate tax prep for their existing customers faster than a startup could win market share. Tax is also sold once a year, so he had about a one-month window to pivot before the door closed. The pivot became Town, an AI assistant that works from your email and calendar. It found product-market fit fast enough that Ben Horowitz messaged a16z's Alex Rampell to say it "changed my life." In this conversation, Alex and Jean-Denis cover: - How to know when to kill a business that's working on paper - Why Town competes with OpenAI and Anthropic, the companies it buys its tokens from - Why the "forward deployed engineer" model gets worse as AI gets better - How a $1/month AI routine can save a restaurant thousands - What agents mean for Amazon, advertising and the future of shopping 0:57 How Ben Horowitz discovered Town 2:20 Why Town started with AI tax prep 3:55 Why tax couldn't be venture scale 6:30 Shutting down a working business 10:24 The customer ask that became Town 13:35 Why Town built for users before IT 16:18 AI's iPhone moment at work 21:28 Jevons paradox for executive assistants 23:13 Town as a "token allocation machine" 28:00 How Town tailors itself to each job 34:03 Competing with your token suppliers 42:24 Staying off the AI labs' radar 44:42 Why AI isn't spreading evenly yet 52:32 How agents break the ad model 55:35 The friction humans actually enjoy 57:05 Why supply aggregators lose to agents YouTube: youtu.be/o_5f4N6Nq80 @jgreze @TownAI @arampell
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Shivam Sinha retweeted
Jean-Denis Greze was CTO of Plaid. His next startup, an AI tax product for small businesses, brought in nearly $1M in revenue its first year, and he projected $2.5 to $3M the next. He shut it down anyway. The reason: in the age of AI, it was never going to be venture scale. Incumbents could use the same technology to automate tax prep for their existing customers faster than a startup could win market share. Tax is also sold once a year, so he had about a one-month window to pivot before the door closed. The pivot became Town, an AI assistant that works from your email and calendar. It found product-market fit fast enough that Ben Horowitz messaged a16z's Alex Rampell to say it "changed my life." In this conversation, Alex and Jean-Denis cover: - How to know when to kill a business that's working on paper - Why Town competes with OpenAI and Anthropic, the companies it buys its tokens from - Why the "forward deployed engineer" model gets worse as AI gets better - How a $1/month AI routine can save a restaurant thousands - What agents mean for Amazon, advertising and the future of shopping 0:57 How Ben Horowitz discovered Town 2:20 Why Town started with AI tax prep 3:55 Why tax couldn't be venture scale 6:30 Shutting down a working business 10:24 The customer ask that became Town 13:35 Why Town built for users before IT 16:18 AI's iPhone moment at work 21:28 Jevons paradox for executive assistants 23:13 Town as a "token allocation machine" 28:00 How Town tailors itself to each job 34:03 Competing with your token suppliers 42:24 Staying off the AI labs' radar 44:42 Why AI isn't spreading evenly yet 52:32 How agents break the ad model 55:35 The friction humans actually enjoy 57:05 Why supply aggregators lose to agents YouTube: youtu.be/o_5f4N6Nq80 @jgreze @TownAI @arampell
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Shivam Sinha retweeted
We're releasing AgentVFS, a virtual filesystem interface for AI agents agentvfs.org/ AgentVFS unifies every service into one interface and replaces the need of calling MCP. Stored data becomes one virtual filesystem across services, described by VFS.md, and actions become virtual CLIs, so the agent no longer calls MCP tools at all. AI agents work on live data across every service through the terminal-like interface they know best. They use the Unix tools they learned in training (ls, grep, jq, pipes, loops) and the terminal workflows they practiced in post-training on agentic coding. We're also releasing VFS-Bench 1.0 agentvfs.org/benchmark/vfs-b…, the first systematic study and benchmark to compare a virtual filesystem with MCP on the same tasks. Compared with MCP, AgentVFS uses up to 88% fewer tokens and has about a 5% higher success rate on average.
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