21, fullstack eng || ex Product Intern @Adobe
India
Joined December 2022
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harsh deep retweeted
I do not fear failure. I fear mediocrity.
I will either end up building billion dollar company(s) or be homeless
I've got 200$ on @digitalocean that will expire in a month, suggest something cool 👀🙌🏻
huge W @SarvamAI
We're thrilled to announce that we have raised $234M in the first close of our $300M Series B at a $1.5B valuation.
@HCLTech and @BessemerVP have joined us in this round, alongside continued support from @khoslaventures and @peakxvpartners
For countries and companies, sovereign control on the AI stack is no longer an optionality. Sarvam will be the partner of choice for this aspiration. The capital allows us to accelerate our momentum towards this full stack of models, compute, and deployments.
A huge thank you to our customers, partners, investors, and the Sarvam team for your trust and belief in what we are building. We’re just getting started.
Read more: sarvam.ai/announcing-series-…
harsh deep retweeted
Indian firms can get GPUs, which is not a problem (for now), but talent is a big, big problem. Indian companies just do not have it in them to pay people multi-million dollar compensation. An equally difficult problem is to convince such people to work in India. I feel that even if Silicon Valley-level comp was on offer, most people in that league would baulk at the idea of living in Indian cities. The solution is to open an office in Dubai or Singapore. That could also open the door to non Indian talent. A $10B spend is possible if 3-4 Indian ITES companies combine forces.
To train a GPT class 1T model from scratch - including failed runs, data acq+clean+rlhf, post-training, team/people will likely req $250M of compute on an aggressive 3-4mo schedule (i.e. more reserved GPUs), $500-600M all-in IF you do a dense one. MoE + fp8 will cut costs by 1/10th depending on how many active params you have. If you want SOTA however, the budgets go significantly higher on test-time compute, post-training RL, and data/synthetic generations..and v. high on talent. Maybe $2-4B all-in. After that comes serving the model. The talent is key to get to SOTA/beat it - and then you have to ensure this is useful enough to have inference vol over time - for which the capital will come if there is usage / TAM. So this is not as much about raising $50-60B, or raising it all at once as the OP says - we are investors in mistral, sarvam, reflection and anthropic - and they all scaled capital over time as models got adoption, but the early bottleneck is more on talent + GPUs at that scale where you can do interesting things.
harsh deep retweeted
🌘 Kimi-K2.7-Code, our latest coding model, is now released and open-sourced!
🔷 Improved coding & agent performance over K2.6: +21.8% on Kimi Code Bench v2, +11.0% on Program Bench, and +31.5% on MLS Bench Lite.
🔷 Reasoning efficiency: Less overthinking, with 30% lower reasoning-token usage compared to K2.6.
🔷 Long-horizon coding: Improved instruction following, higher end-to-end coding task success rates.
⚡️ 6x High-Speed Mode coming soon!
🔌 Available today via Kimi API and Kimi Code.
🔗 Kimi Code: kimi.com/code
🔗 API: platform.moonshot.ai
⚡
Introducing Claude Opus 4.8: it builds on Opus 4.7 with sharper judgment, more honesty about its own progress, and the ability to work independently for longer than its predecessors.
Available today at the same price.
ALT Benchmark table showing how Claude Opus 4.8 compares to its predecessor and to other models on tests of coding, agentic skills, reasoning, and practical knowledge work tasks.
