
co-founder & cto https://xitter.cf/t.co/AYn0czWxZ1 | Prev ML & Systems @Stanford
Joined April 2024
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@via_ai_ is the first product to provide frontier AI coding accuracy at half the cost.
Claude Code is 306% the cost of Via at the same accuracy on DeepSWE v1.1. Check out Ila’s post below for more detailed benchmarks.
We’re already in trials at several of the largest tech companies in Silicon Valley and are in production at a public healthcare company.
Via is powered by Codegraph (our LLM-optimized compression algorithm that led us to be #1 on SWE-Bench Verified for several months last year) and our new Umami agent architecture.
Via integrates with your favorite IDE/CLI in one terminal command, is deployed within your VPC, and has an unchanged dev experience.
We are a small team of Olympiad camp qualifiers (with multiple gold medals), researchers, and repeat founders, backed by leaders like Jeff Wilke (ex-CEO Amazon Worldwide Consumer), Manik Gupta (ex-Uber CPO), and others from Anthropic, Google, Uber, Apple, Snowflake.
Via is designed for enterprises with 7 figure coding budgets and above. If this applies to you, DM me or email pilot@via.dev and we’ll get back to you very soon.
Stay tuned for a deep dive into Codegraph and Umami.
Today, @via_ai_ is coming out of stealth to provide enterprises frontier AI coding at half the cost.
We are a team of Olympiad camp qualifiers (with multiple gold medals), researchers, and repeat founders from Stanford and IIT.
Via is backed by @jeffawilke (ex-CEO Amazon Worldwide Consumer), @manikgupta (ex-Uber CPO), @shaileshrao (ex-Google MD), and other legends from Anthropic, Apple, Snowflake, and Twitter.
@ShrayAlag and I left Stanford CS to build Codegraph, an LLM-optimized compression algorithm. Codegraph put Via at the top of SWE-Bench Verified for several months of 2025, ahead of Anthropic, OpenAI, Cognition, and Cursor (single model, no test-time compute).
Now, we’re bringing this research, alongside our new Umami agent architecture, to market. Via integrates in 10 seconds with any CLI / IDE and runs within your enterprise’s VPC.
On benchmarks, Via and Via US (US-only models) set a new standard of performance:
- On DeepSWE v1.1, Claude Code (Opus 5.5) is 306% the cost of Via US. Via US is equally accurate.
• Devin Fusion is 244% the cost of Via US, Cursor Router is 294%.
- On Terminal Bench 4.0, Claude Code (Opus 5.5) is 150% the cost of Via. Via is more accurate.
• Devin Fusion is 155% the cost of Via, Cursor Router is 134%.
We are in production at a public healthcare company, with trials underway at several of the largest tech companies in Silicon Valley.
We have room for 5 more enterprises before the end of the year. DM me or email pilot@via.dev and we’ll get back to you very soon.
See the comments for how Via would deploy for your team.
Thank you for the kind words Rajan and all of the support you have shown us along the journey so far! Can’t wait to bring this to more enterprises and fight the token-maxxing epidemic!
Congratulations @ilaamathur @ShrayAlag and team @via_ai_ Coding is the mother of all AI use cases with 90% of AI spends in most enterprises going to AI coding. Here comes @via_ai_ that lets developers use what they are using, but cuts the costs by 25% - 40%. If you are worried about escalating AI spends on coding, look no further!
Thank you for believing in us at the earliest stages @shaileshrao.
I’m so grateful for your support and guidance as we take on this incredible opportunity and continue to push the frontier.
Go @via_ai_ and Arc180! Can’t wait for what’s to come… onwards and upwards
Proud to back @ilaamathur and @ShrayAlag, Founders of @via_ai_ from my fund Arc180. @via_ai_ helps enterprises take full advantage of frontier AI coding at half the cost already working with some of the biggest tech companies. Ping me if you want to discuss
Very grateful for your support and guidance. It’s a privilege to get to learn from you and can’t wait for what’s ahead! @jeffawilke
Manik, thank you for the kind words and more importantly for believing in us early on.
I’m incredibly grateful to be able to learn from you. Excited to keep working on this important problem!
Congrats @ilaamathur, @ShrayAlag and the entire @via_ai_ team!
It's been so inspiring to see you all build both the team and the product over the last few months. Grateful to be a small part of your journey!
Ishan is cracked. Super exciting stuff!
Excited to announce Next-Concept Modeling (NCM): our new paradigm for fast and parallel, coarse-to-fine text generation! 🚀
Compared to strong diffusion language models (DLMs) at similar parameter sizes, NCM showcases:
⚡ 31× faster generation on downstream summarization and translation tasks.
🏎️ 10–21× faster unconditional generation across TinyStories, LM1B, and OpenWebText, with comparable or better generative perplexity using 3–5× fewer training tokens.
🧩 Better reasoning performance on tasks like Sudoku.
Check out the paper and codebase 👇
Arxiv: arxiv.org/abs/2610.14162
AlphaXiv: alphaxiv.org/abs/2610.14162
Code: github.com/HazyResearch/ncm
Enjoyed working with @KumbongHermann and advised by @simran_s_arora @Azaliamirh and @HazyResearch.
Shray Alag retweeted
Excited to announce Next-Concept Modeling (NCM): our new paradigm for fast and parallel, coarse-to-fine text generation! 🚀
Compared to strong diffusion language models (DLMs) at similar parameter sizes, NCM showcases:
⚡ 31× faster generation on downstream summarization and translation tasks.
🏎️ 10–21× faster unconditional generation across TinyStories, LM1B, and OpenWebText, with comparable or better generative perplexity using 3–5× fewer training tokens.
🧩 Better reasoning performance on tasks like Sudoku.
Check out the paper and codebase 👇
Arxiv: arxiv.org/abs/2610.14162
AlphaXiv: alphaxiv.org/abs/2610.14162
Code: github.com/HazyResearch/ncm
Enjoyed working with @KumbongHermann and advised by @simran_s_arora @Azaliamirh and @HazyResearch.
🚀 Introducing Next-Concept Modeling (NCM): our new paradigm for fast, parallel, coarse-to-fine text generation.
Compared to strong diffusion language models (DLMs) at similar parameter size, NCM achieves:
⚡ Up to 31× faster generation on downstream tasks like summarization and machine translation with comparable or better quality.
🏎️ 10–21× faster unconditional generation across TinyStories, LM1B, and OpenWebText, with comparable or better generative perplexity and using 3–5× fewer training tokens.
🧩 Better performance on reasoning tasks like Sudoku, outperforming both AR and diffusion baselines with fewer sampling steps.
With @ishanskhare, @IddahMlauzi, @ekellbuch, @simran_s_arora, @Azaliamirh, and @HazyResearch.
More details in the thread 🧵👇
excited to share more soon
McKinsey reports that 93% of enterprises have exceeded their AI budgets. Spend is only increasing
Getting more out of AI shouldn’t mean paying more for it. Open source models and Fable/Astra alternatives offer much more intelligence per dollar. But routing between them can break prompt caching and disrupt the developer experience.
Our team of Olympiad medalists, ex-founders, and researchers has spent the last year building algorithms to cut coding costs without sacrificing frontier accuracy or the experience developers love.
More soon
mckinsey.com/quarterly/the-f…
OpenAI’s $200 subscriptions will only give you 1/2 the usage starting tomorrow.
The age 50x and 70x subsidizations is over. All of their labs have been pushing enterprises and are now gradually pushing consumers to api pricing before their IPOs. (Enterprises were recently notified that all of their current subscription pricing would be moving to api based starting next year)
It’s far better to be in control of your coding system and optimize intelligence/$ than letting labs gradually degrade you towards api pricing.
Hi,
Tomorrow we are re-opening the Pro $200 subscriptions to new subscribers, but together with it we are also changing how we calculate the usage for it. In effect, if you do the math, it will net out at half the dollar in API spend compared to the old Pro $200 plan.
Now that it's said, let me explain why this is happening and why you will still get more work done than if you were on the Pro $200 subscription one month ago.
(a) We didn't want to compromise in other ways and are committing to not reintroducing the 5h limit, so that you can fully use the weekly usage when you want.
(b) On the subscription, we guarantee that over time you always get more work done and with an increasing level of quality. This means that you will continue to get more value per dollar spent as a result of models getting more efficient and us passing down the improvements in the form of API price reductions.
(c) We don't want to put an incentive on ourselves to artificially inflate the API list prices to make it look like you are getting a lot (and workaround it through discounts, etc). Instead we want to continue to both rapidly reduce prices and increase capabilities of models on the API. This week we introduced GPT-6 Sol and GPT-6 Luna at 50% of their previous price. Over time, we see prices go low enough that it makes sense for most to buy usage as needed without there being a significant gap between what you get in a subscription and what you get in the API for a dollar spent.
(d) Tomorrow, we are adding more things to the subscription that won't draw on the usage, I won't reveal what that is yet.
I wanted to be transparent before all the big announcements tomorrow. Lots of new exciting things are coming to the subscriptions that will make it super compelling, but I wanted to make sure to share this change ahead of time so you can all understand it before we shower you with good news.
Codexingly,
Tibo
Shray Alag retweeted
Introducing Kimi K3: Open Frontier Intelligence
🔹 2.8 Trillion Parameters, 1 Million Context, Native Multimodal
🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts
🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional cost
🔹 Built for long-horizon agentic coding and self-evolving workflows
Kimi K3 is now live on on Kimi.com, Kimi Work, Kimi Code, and the Kimi API.
Open Weights by July 27, 2026.
🔗 API: platform.kimi.ai
🔗 Tech blog: kimi.com/blog/kimi-k3
Unfortunately it appears the world has changed and we are never going back
OpenAI just announced GPT-5.6 Sol, a model that beats Mythos at 1/3 the price
It will only be in limited release to start as the government reviews it
The days of wide release frontier models are over
The years of some executives shilling AI as a world destroying technology that needs regulation got what they wanted, regulation
Now only the select few will get access to super intelligence. Leaving the normie class behind
It's a massive loss. Now winners and losers will be picked by the government. Which sucks.
All of this doomerism has done nothing but slow America down
On the positive side, Fable 5 will have competition
It appears OpenAI has discovered a new post training technique that is allowing them to make revolutionary jumps at a fraction of the price
That is unbelievably positive for all consumers.
Will be counting down the days until I get to use this model
In the meantime I hope this was a wake up call to the entire industry that our words and marketing matter
Introducing a limited preview of GPT-5.6 Sol, our next generation frontier model, as well as GPT-5.6 Terra, a balanced model for efficient, everyday work, and GPT-5.6 Luna, a fast and affordable model for high-volume work.
openai.com/index/previewing-…
Most companies aren't that far behind. Most likely you’re not either.
We're in the middle of a massive economic renovation - slow, confusing, time-consuming - but, if history is any guide, the future belongs to the people who roll up their sleeves and work on what comes next.
Read the full report: ntn.so/2026-research
Shray Alag retweeted
Introducing the Fusion API, the smartest compound model in the market.
Fusion achieves Fable-level intelligence at half the price.
How it works 👇
Shray Alag retweeted
Good take
My guess is
- demand for intelligence is near infinite
- but 80% of workloads will be running on 99% cheaper models within 12-18 months
- 20% of workloads will still run on latest gen models where IQ maxing is important (scientific breakthroughs, higher level ochestrator agents?)
- rough analogy might be what % of macbooks or gaming PCs sold have the maxed out specs for CPU/GPU, prices are falling much faster than Moore's law here though
- this leads me to think the limiting factor will be energy and compute, not better models
At Coinbase we're working hard on routing prompts to cheaper models where appropriate, and in some cases have been able to keep costs roughly flat, while token usage continues to grow exponentially.
The most basic way AI could blow up imo. I'm not saying it does but this is the most obvious way I can see it happening
- Per seat subscriptions are massively subsidized. The flat fee was priced way below what heavy usage actually costs
- For real business use you have to move to the API anyway. Data protections, work integrations and compliance officer approval
- On the API you pay metered rates, and businesses are burning credits way faster than the per seat pricing ever led them to expect
- This is everywhere right now. Internally for us, Codex users, Uber torching its entire 2026 AI budget in 4 months, the Microsoft comments. Just go try an API
I shared more on this here: xitter.cf/Shaughnessy119/status/…
- And I don't think most businesses have the money to keep paying increasing API rates without a real change to how they operate (caps needed)
- Because they have a cheap alternative. They can reach open source models through any aggregator (OpenRouter, Venice, Baseten, Together) and still get strong privacy. Venice private data centers, or E2EE/TEE serving GLM 5.1.
More on open source inference provider raises here: xitter.cf/Shaughnessy119/status/…
- And the discount is enormous. DeepSeek V4 codes within a hair of Opus on SWE bench at roughly 1/30th the price, and the cheapest open models run closer to 1/100th
- Chinese labs open source frontier grade models. The model is the single biggest cost an inference provider has, and they get it for free
- This idea dies if China goes closed source. That is actually bullish web2 AI labs, because if everyone is closed you pay up for the best intelligence. China goes closed source if they are tired of giving away an asset and they want the revenue and data flow to train new models
- Is this showing up in web2 AI lab revenue yet? No. Revenue is off the charts. Anthropic went from 9B to 47B run rate in five months
- So go forward, what happens?
- I think revenue slowly starts leaking to the open source inference providers (see Venice usage, OpenRouter's $113M raise, Baseten is raising at $11B or triple its valuation in three months, on revenue that went from $200M to $600M annualized in a single quarter)
- It doesnt move overnight, but it caps the labs ability to raise prices, and margins are already deeply negative. OpenAI is reportedly running near negative 122%
- With margins that bad there is no cash flow, so the labs are fully dependent on outside capital to buy GPUs, train models, and keep subsidizing usage (I.e. see Google tapping $80b equity sale, granted 30b for employee RSU taxes. Clearly they think Equity is overvalued or you wouldn't sell it)
- The break comes when that capital stops. Pricing is capped so margins cant improve, and the moment investors lose conviction on payback, the whole flow reverses
- Why would they lose conviction on payback? Back to the start - the inability to improve margins or get businesses to pay more
- This is also limiting, if we start making new drugs with AI or create entirely new businesses, you better believe people will pay up to the max for AI usage
Shray Alag retweeted
imo there’s a pretty solid default recipe that everyone should use to optimize a system of
Agent = Model + Harness
you should “train” both
1. Build v1 agent using a sensible base harness and some task specific prompting + tools
2. Harness Engineering using eval tasks that roughly match prod
this is often enough - most companies can get acceptable perf doing this. then they collect traces, mine them for patterns, and make slight tweaks from there
3. SFT using data collected from traces) or synthetic data. Often is good candidate for “distillation tasks” to train a cheaper model while maintaining existing performance
4. RL if you have the bandwidth and ability and desire to create environments and designing rewards that represents the tasks you want your agent to be good at. Push past the SFT behavior of “copying” data from existing model to pushing past in some dimension
5. Light harness engineering again to squeeze any more juice (ex: slight prompting) using the trained model that’s better at your task distribution
this loop will largely be productized as a general purpose recipe for building and improving agents
we’re still in the earliest innings of the world’s companies getting comfortable with steps 1-2 of this loop. Harness engineering will probably be the dominant way ppl will optimize agents
but i expect a large number of companies to onboard through this entire loop on some trial project of interest in the next year
Shray Alag retweeted
As I wrote this, I saw X go into meltdown over tokens.
You've seen the headlines: “Uber blows yearly AI budget in just one quarter.” “Meta employee burns 281 billion tokens in April.”
But, the problem isn't spending. Spending works. Since 2023, the top quartile of our AI spenders doubled their revenue. The bottom quartile? Flat.
It's blind spending. We don’t know which spend worked.
A sales team has qualified leads. A support team has resolved conversations. These are units you can measure against. All a token tells you is the meter ran, not whether the work was worth it or not.
Finance says, “half the budget,” engineering says, “double it” and you don’t know who’s right because there is no shared language of value. There’s no attribution, and no attribution means no allocation.
For example, right now, all work, no matter the size or shape, defaults to frontier models. But meeting summaries and calendar updates don’t require GPT-5.5 Pro.
In isolation this seems trivial, but re-route just 10% of a $10M AI bill from frontier to GPT-4 level intelligence you’ve saved nearly one million dollars. This sounds like a made-up stat — it’s not. It truly is that much cheaper.
This is the future of finance: not blindly rubber-stamping or rejecting AI spend, but allocating it with the same rigor companies apply to headcount.
Shray Alag retweeted
Delve, a YC-backed compliance startup that raised $32 million, has been accused of systematically faking SOC 2, ISO 27001, HIPAA, and GDPR compliance reports for hundreds of clients. According to a detailed Substack investigation by DeepDelver, a leaked Google spreadsheet containing links to hundreds of confidential draft audit reports revealed that Delve generates auditor conclusions before any auditor reviews evidence, uses the same template across 99.8% of reports, and relies on Indian certification mills operating through empty US shells instead of the "US-based CPA firms" they advertise. Here's the breakdown:
> 493 out of 494 leaked SOC 2 reports allegedly contain identical boilerplate text, including the same grammatical errors and nonsensical sentences, with only a company name, logo, org chart, and signature swapped in
> Auditor conclusions and test procedures are reportedly pre-written in draft reports before clients even provide their company description, which would violate AICPA independence rules requiring auditors to independently design tests and form conclusions
> All 259 Type II reports claim zero security incidents, zero personnel changes, zero customer terminations, and zero cyber incidents during the observation period, with identical "unable to test" conclusions across every client
> Delve's "US-based auditors" are actually Accorp and Gradient, described as Indian certification mills operating through US shell entities. 99%+ of clients reportedly went through one of these two firms over the past 6 months
> The platform allegedly publishes fully populated trust pages claiming vulnerability scanning, pentesting, and data recovery simulations before any compliance work has been done
> Delve pre-fabricates board meeting minutes, risk assessments, security incident simulations, and employee evidence that clients can adopt with a single click, according to the author
> Most "integrations" are just containers for manual screenshots with no actual API connections. The author describes the platform as a "SOC 2 template pack with a thin SaaS wrapper"
> When the leak was exposed, CEO Karun Kaushik emailed clients calling the allegations "falsified claims" from an "AI-generated email" and stated no sensitive data was accessed, while the reports themselves contained private signatures and confidential architecture diagrams
> Companies relying on these reports could face criminal liability under HIPAA and fines up to 4% of global revenue under GDPR for compliance violations they believed were resolved
> When clients threaten to leave, Delve reportedly pairs them with an external vCISO for manual off-platform work, which the author argues proves their own platform can't deliver real compliance
> Delve's sales price dropped from $15,000 to $6,000 with ISO 27001 and a penetration test thrown in when a client mentioned considering a competitor
A detailed and brutal look at the tactics of buzzy AI compliance startup Delve
"Delve built a machine designed to make clients complicit without their knowledge, to manufacture plausible deniability while producing exactly the opposite."
substack.com/home/post/p-191…
Shray Alag retweeted
Cursor is raising at a $50 billion valuation on the claim that its “in-house models generate more code than almost any other LLMs in the world.” Less than 24 hours after launching Composer 2, a developer found the model ID in the API response: kimi-k2p5-rl-0317-s515-fast.
That’s Moonshot AI’s Kimi K2.5 with reinforcement learning appended. A developer named Fynn was testing Cursor’s OpenAI-compatible base URL when the identifier leaked through the response headers. Moonshot’s head of pretraining, Yulun Du, confirmed on X that the tokenizer is identical to Kimi’s and questioned Cursor’s license compliance. Two other Moonshot employees posted confirmations. All three posts have since been deleted.
This is the second time. When Cursor launched Composer 1 in October 2025, users across multiple countries reported the model spontaneously switching its inner monologue to Chinese mid-session. Kenneth Auchenberg, a partner at Alley Corp, posted a screenshot calling it a smoking gun. KR-Asia and 36Kr confirmed both Cursor and Windsurf were running fine-tuned Chinese open-weight models underneath. Cursor never disclosed what Composer 1 was built on. They shipped Composer 1.5 in February and moved on.
The pattern: take a Chinese open-weight model, run RL on coding tasks, ship it as a proprietary breakthrough, publish a cost-performance chart comparing yourself against Opus 4.6 and GPT-5.4 without disclosing that your base model was free, then raise another round.
That chart from the Composer 2 announcement deserves its own paragraph. Cursor plotted Composer 2 against frontier models on a price-vs-quality axis to argue they’d hit a superior tradeoff. What the chart doesn’t show is that Anthropic and OpenAI trained their models from scratch. Cursor took an open-weight model that Moonshot spent hundreds of millions developing, ran RL on top, and presented the output as evidence of in-house research. That’s margin arbitrage on someone else’s R&D dressed up as a benchmark slide.
The license makes this more than an attribution oversight. Kimi K2.5 ships under a Modified MIT License with one clause designed for exactly this scenario: if your product exceeds $20 million in monthly revenue, you must prominently display “Kimi K2.5” on the user interface. Cursor’s ARR crossed $2 billion in February. That’s roughly $167 million per month, 8x the threshold. The clause covers derivative works explicitly.
Cursor is valued at $29.3 billion and raising at $50 billion. Moonshot’s last reported valuation was $4.3 billion. The company worth 12x more took the smaller company’s model and shipped it as proprietary technology to justify a valuation built on the frontier lab narrative.
Three Composer releases in five months. Composer 1 caught speaking Chinese. Composer 2 caught with a Kimi model ID in the API. A P0 incident this year. And a benchmark chart that compares an RL fine-tune against models requiring billions in training compute without disclosing the base was free.
The question for investors in the $50 billion round: what exactly are you buying? A VS Code fork with strong distribution, or a frontier research lab? The model ID in the API answers that.
If Moonshot doesn’t enforce this license against a company generating $2 billion annually from a derivative of their model, the attribution clause becomes decoration for every future open-weight release. Every AI lab watching this is running the same math: why open-source your model if companies with better distribution can strip attribution, call it proprietary, and raise at 12x your valuation?
kimi-k2p5-rl-0317-s515-fast is the most expensive model ID leak in the history of AI licensing.
Clickbaity but the age of technical moats is soon going to be over. We need to start thinking about moats as the team, customers, and data.
Short every SaaS company on planet earth
Today Cursor announced a REALLY sick feature that probably cost them millions of dollars to make
Their AI agent records demo videos of itself after it builds things
I gave the announcement to my OpenClaw. It built it out in 5 minutes.
I pasted in the announcement to Henry. He said on it chief. 5 minutes later he not only built out the entire feature, but recorded a demo video of it too
It's now implemented into our entire workflow. Now every time I ask my OpenClaw to build something, a demo video will be attached to every PR
At this point how does any SaaS survive? You can take quite literally any feature they build, give it to your personal assistant, and it's built out in 5 minutes
What moat is left? When I have superintelligence running locally on my mac studio, and it's able to build out any piece of software I can imagine in minutes, literally what value is left in any software company?
This is the most exciting, frightening, awe inspiring time to ever be alive
soon the accuracy of these models will be strong enough to complete 95% of tasks even at a 3B level and speed will be the dominating factor
🚀 Introducing the Qwen 3.5 Medium Model Series
Qwen3.5-Flash · Qwen3.5-35B-A3B · Qwen3.5-122B-A10B · Qwen3.5-27B
✨ More intelligence, less compute.
• Qwen3.5-35B-A3B now surpasses Qwen3-235B-A22B-2507 and Qwen3-VL-235B-A22B — a reminder that better architecture, data quality, and RL can move intelligence forward, not just bigger parameter counts.
• Qwen3.5-122B-A10B and 27B continue narrowing the gap between medium-sized and frontier models — especially in more complex agent scenarios.
• Qwen3.5-Flash is the hosted production version aligned with 35B-A3B, featuring:
– 1M context length by default
– Official built-in tools
🔗 Hugging Face: huggingface.co/collections/Q…
🔗 ModelScope: modelscope.cn/collections/Qw…
🔗 Qwen3.5-Flash API: modelstudio.console.alibabac…
Try in Qwen Chat 👇
Flash: chat.qwen.ai/?models=qwen3.5…
27B: chat.qwen.ai/?models=qwen3.5…
35B-A3B: chat.qwen.ai/?models=qwen3.5…
122B-A10B: chat.qwen.ai/?models=qwen3.5…
Would love to hear what you build with it.








































