Building @saga_HQ AI collaborative workspaces

London & Paris
Joined July 2014
Filip Stanev retweeted
Signups are now open on Code[dot]Storage. Today, more new repos are created per second on Code Storage than any other platform. Lovable, Bolt, Amp, Poke, and many more of the largest agentic workloads store their code and agent memory at scale using Code.Storage.
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Filip Stanev retweeted
The fallacy of this is that more creates more. More hours, more hiring, more something. And it is true in a sense. If you put in more work, more work will happen. But I think for most startups, the leverage is really in how differently you approach the problem, how well you cultivate your team, and the strategy. Any large company can outspend you on hours. They have thousands or tens of thousands more people, spending more hours. If hours worked were the metric, every large company and government organization would always win and do the best work. More hours, better output. This thinking is often representative of younger founders, where the startup becomes their identity and life. They have a hard time doing anything else, and cannot understand that your work is not the person that is you. But activities outside of work can grow you as a person too and make you do better work. I’ve never worked this way. As a designer, I always saw the need to take a step back, to take a break. At times, I might work 12 hours or 16 hours, or whatever amount was needed, but it wasn’t the norm. You just can't grind design, you need inspiration. But taking that step away from the work, would give me more perspective, inspiration and I could approach the problem differently or I could just see the solution. Grinding is never good for any creative problem, and startups or creating new products are often mostly about creative problem solving. Grinding works ok for email jobs, or where you just executing on very clear playbook. With Linear, we’ve never worked this way. We work reasonable hours, 5 days a week. All of us founders have families. Many of our employees have families. I personally stop every evening, spend time with the family, cook dinner for the family, eat dinner together, and focus on things outside of work. Sometimes I work in the late evenings or weekends, but to me the pride is that I don’t need to. Company should be succesful without it. My goal is to build a company that is sustainable in the long term, and doesn’t require heroics or personal sacrifices every single day. There are times when our team is heroic. Launches, incidents, some other work that just needs to be done. They will work late into the night because they know it is the right thing. But we don’t require that every day or every week, and the more this happens, the more I think it is a failure of our company and leadership. The team and the leaders should always keep a reserve to use when something is needed. Our thinking was also that quality, which we value, doesn’t emerge from working more or stressing people more. It emerges when you create the conditions for it to emerge. Often it is the appreciation, space, time, and how the person feels. A person who is rested will do better work. I wouldn’t attribute much of our success to working a lot. The success came from having clear thinking, ideas, and focus to do the right things. I sometimes wish we could move the culture more toward a Zen master. Real mastery is not exerting the most effort. It is achieving the outcome with the least necessary effort.
"If you are not working 7 days per week, you are going to lose". Corgi Insurance is the most intense workplace culture in startups. - The company works 7 days per week. - Founder (@nico_laqua) lives and sleeps in the office. - He built a cafe in the office because there was no local cafe that was open 24/7. - 2/3 of the first 30 team members have the Corgi logo as a tattoo. Today I went behind the scenes with Nico, who has used this culture to scale the company to a $2.6BN valuation in just two years. My condensed notes below: 1. If You Are Not Working 7 Days Per Week, You Are Going to Lose: Whatever you can get done in 5 days, you'll get more done in 6 and 7. If you are trying to solve the world’s hardest problems, a standard 5-day workweek will not cut it. 2. Work Trials Repel the Mediocre: Corgi forces candidates into mock work trials over the weekend. If seeing a full office on a Saturday scares them, they don't belong. True intensity acts as a natural filter to attract killers and repel clock-watchers. 3. Lead from the Front Lines You can’t demand 7-day weeks while sitting on a yacht. Nico sleeps 3–4 hours a night on a mattress inside the office. If you want your troops to bleed, you have to be in the trenches with them. 4. Culture Only Means One Thing: Winning Forget superficial jargon like "hackers" or "ex-founders." Strip away the corporate fluff. A great startup culture is aggressively optimized around one single word: Winning. 5. Lifespan vs. Victories Building something world-historic requires radical sacrifice. When asked if he'd rather build a trillion-dollar company and die at 50, or fail and live to 80, the answer was easy. "I would rather measure my lifespan in victories." 6. Reject the Comfort of "Quiet Quitting." If you are operating in a hyper-growth environment and your days off happen to be Saturday and Sunday every single week, you are quiet quitting. To win, you must deliberately bypass the off-ramps of personal comfort and low volatility. Corgi isn't for everyone—and that’s exactly the point.
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The sign language interpreter absolutely nailed BANGARANGA.
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Filip Stanev retweeted
all technology brothers should have a birthright trip to paris to see how good certain things can get and all the axes of civilization they don’t think about. they should also get one to singapore to witness the hollow Disneyland feigned joy of technocratic perfection
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Filip Stanev retweeted
After 4 years, we’re announcing Instant 1.0. Instant is the best backend for AI-coded apps. Let us tell you why.
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Filip Stanev retweeted
Excited to launch Gemma 4: the best open models in the world for their respective sizes. Available in 4 sizes that can be fine-tuned for your specific task: 31B dense for great raw performance, 26B MoE for low latency, and effective 2B & 4B for edge device use - happy building!
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Filip Stanev retweeted
llama.cpp at 100k stars now that 90% of the code worldwide is being written by AI agents, I predict that within 3-6 months, 90% of all AI agents will be running locally with llama.cpp 😄 Jokes aside, I am going to use this small milestone as an opportunity to reflect a bit on the project and the state of AI from the perspective of local applications. There is a lot to say and discuss and yet it feels less and less important to try to make a point. Opinions about viability of local LLMs are strongly polarized, details are overlooked, the scientific approach is lacking. Arguments are predominantly based on vibes and hype waves. One thing is clear though - local LLMs are used more and more. I expect this trend to continue and likely 2026 will end up being one of the most important years for the local AI movement. I admit that I didn't expect the agentic era to come so quickly to the local LLM space. One year ago, the available models were too computationally expensive for doing long-context tasks. There wasn't an obvious path towards meaningful agentic applications. The memory and compute requirements were huge. Last summer, with the release of gpt-oss, things started to change. It was the first time we saw a glimpse of tool calling that actually works well within the resource constraints of our daily devices. Later in the year, even better models were released and by now, useful local agentic workflows are a reality. Comparing local vs hosted capabilities at a given moment of time is pointless. To try put things into perspective: - We don't need frontier intelligence to automate searches and sending emails - We don't need trillion parameter models to be able to summarize articles or technical documents - We don't need massive GPU data centers to control our home appliances or turn the lights off in the garage I believe that there is a certain level of intelligence we as humans can comprehend and meaningfully utilize to improve our working process. Beyond that level, access to more intelligence becomes unnecessary at best and counterproductive at worst. I also believe that that level of useful artificial intelligence is completely within reach locally and it has always been just a matter of implementing the right software stack to bring it to the end user. With llama.cpp, I am confident that we continue to be on the right track of building that software stack! The llama.cpp project is going stronger than ever. With more than 1500 contributors, the project keeps growing steadily. From technical point of view, I think that llama.cpp + ggml is the only solution that actually makes sense. That is, the software stack must run efficiently on every possible device, hardware and operating system. The technology is too important to be vendor-locked. It has to be developed in the open, by the community, together with the independent hardware vendors. This is the only right way to build something that will truly make a difference in the long run. I won't try to convince you about what is currently and will be possible with local AI. We will just continue to build as usual. I am confident that after the smoke clears and we look objectively at what we have built together, the benefits will be obvious to everyone. Big shoutout to all llama.cpp maintainers. I feel extremely lucky to be able to work together with so many talented contributors. Every day I learn something new and I feel there is so much more cool stuff that we are going to build. Also, I am really thankful that the project continues to have reliable partners to support it! Cheers!
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Filip Stanev retweeted
seems obvious but: things that are changing rapidly: 1. context windows 2. intelligence / ability to reason within context 3. performance on any given benchmark 4. cost per token things that are not changing much: 1. humans 2. human behavior, preferences, affinities 3. tools, integrations, infrastructure 4. single core cpu performance therefore, ngmi: 1. "i found this method to cut 15% context" 2. "our method improves retrieval performance 10% by using hybrid search" 3. "our finetuned model is cheaper than opus at this benchmark" 4. "our harness does this better because we invented this multi agent system" 5. "we're building a memory system" 6. "context graphs" 7. "we trained an in house specialized rl model to improve task performance in X benchmark at Y% cost reduction" wagmi: 1. product/ui 3. customer acquisition 4. integrations 5. fast linting, ci, skills, feedback for agents 6. background agent infra to parallelize more work 7. speed up your agent verification loops 8. training your users, connecting to their systems and working with their data, meeting them where they are
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Filip Stanev retweeted
I'm usually not one to write thought pieces without much technical depth. But here we go. Slow the fuck down. mariozechner.at/posts/2026-0…
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Filip Stanev retweeted
Introducing Chroma Context-1, a 20B parameter search agent. > pushes the pareto frontier of agentic search > order of magnitude faster > order of magnitude cheaper > Apache 2.0, open-source
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Filip Stanev retweeted
Code[dot]Storage A new Git provider for machines by @pierrecomputer. In Oct, Github shared they were averaging ~230 new repos per minute. Last week we hit a sustained peak of > 15,000 repos per minute for 3 hours. And in the last 30 days customers have created > 9m repos🧵
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Filip Stanev retweeted
Pi is very cost efficient.
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Filip Stanev retweeted
🎉 Keyboard-first Saga AI You can now use Saga AI faster with better shortcuts, clearer shortcut hints, and smoother editor AI flows. saga.so/updates/keyboard-fri…
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🤘 Rivet Secure Exec (V8 isolates): Securely execute Node.js anywhere (Railway, Vercel, Hetzner, Kubernetes) vs 🔥 Firecracker (microVMs): Full Linux operating system, but requires specialized infrastructure
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Filip Stanev retweeted
Lately, whenever I open this app and see the latest tricks, and hacks, and notes, and workflows, and spec here and skill there, I can't help but think: All of this will be washed away by the models. Every Markdown file that's precious to you right now will be gone.
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Filip Stanev retweeted
Introducing the Secure Exec SDK Secure Node.js execution without a sandbox ⚡ 17.9 ms coldstart, 3.4 MB mem, 56x cheaper 📦 Just a library – supports Node.js, Bun, & browsers 🔐 Powered by the same tech as Cloudflare Workers $ 𝚗𝚙𝚖 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚜𝚎𝚌𝚞𝚛𝚎-𝚎𝚡𝚎𝚌
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Filip Stanev retweeted
MiniMax M2.7 from @MiniMax_AI is live on OpenRouter! M2.7 sees a large jump in agentic and tool calling capabilities.
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Great read! Slop creep is real but you can still reduce it by 1) just talking to the agent, 2) using the best models, and 3) moving in small steps. Today's slop will be fixed by the more elegant slop 6 months from now.
slop creep is what happens when you turn your brain off and hand the thinking to coding agents each individual change is fine, but all together, you have a pile of crap we're witnessing this happen in real-time across everything boristane.com/blog/slop-cree…
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Filip Stanev retweeted
Introducing 𝑨𝒕𝒕𝒆𝒏𝒕𝒊𝒐𝒏 𝑹𝒆𝒔𝒊𝒅𝒖𝒂𝒍𝒔: Rethinking depth-wise aggregation. Residual connections have long relied on fixed, uniform accumulation. Inspired by the duality of time and depth, we introduce Attention Residuals, replacing standard depth-wise recurrence with learned, input-dependent attention over preceding layers. 🔹 Enables networks to selectively retrieve past representations, naturally mitigating dilution and hidden-state growth. 🔹 Introduces Block AttnRes, partitioning layers into compressed blocks to make cross-layer attention practical at scale. 🔹 Serves as an efficient drop-in replacement, demonstrating a 1.25x compute advantage with negligible (<2%) inference latency overhead. 🔹 Validated on the Kimi Linear architecture (48B total, 3B activated parameters), delivering consistent downstream performance gains. 🔗Full report: github.com/MoonshotAI/Attent…
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