20 • AI Engineer @Lenskart_com • 15x Hack Winner 🏆

🐲/acc
Joined March 2020
One more Rat join the Race!
We’re opening a research preview of Claude Duet, a new app where Claude works as a proactive agent. It comes to you with what needs your attention, completes tasks you ask of it, keeps working when you step away, and checks with you before it acts on your behalf.
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Constrained decoding already means one thing. Keep generating, and mask any token the grammar rejects. ScaleDown's CDMs are a different runtime. Classify does not write the label. It scores the labels you supplied, softmax-normalizes them so they sum to 1, and returns top_label. Use that when the schema is closed. Routing, tagging, fields you already named. Do not treat top_label as abstention. The distribution always has a winner. Refusal is a threshold you set, or an "other" label you added. The model will not invent either. The useful split is not how fast the loop is. It is whether you still need a decoder.
You already gave the LLM the answer. So why is it writing it out from scratch? Introducing a new AI model architecture: Constrained Decoding Models (CDMs) LLMs were built to write. Most production workloads just need an answer: fast, consistent, and affordable. CDMs are purpose-built for 1 task each. They read your input once and skip the token-by-token generative loop that makes LLMs slow and expensive. Not just a decision model, but a family of 3 CDMs live today: • Classify: define your labels and get back a calibrated probability + reason for each. Used for routing, tagging, and intent detection. • Extract: describe the entities in natural language and get back well-formed JSON with confidence scores + citations. Used for web scraping, document processing, and PII redaction. • Summarize: pass in long text, images, or PDFs and get back a consistent summary under 1,000 tokens. Used for context compaction, metadata generation, and transcript digests. Built for production: • 1M tokens of multimodal (text + image) input, 31x larger than Jev • 77.2% on DecisionBench vs Jev's 72% • 200x faster, 467x cheaper than frontier LLM APIs • $0.05/M input tokens. Output is free (yes, even for summarize) Live on your favorite platforms today: @OpenRouter @vercel_dev @LiteLLM @LangChain @TrustedRouter @itsArthurAI @useRouterPlus Get 50M input tokens for free (for the first 50 hours) at link below 👀
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Mohit Goyal (Harness arc) retweeted
they said find a gap in the market. i may have taken it too seriously. (me and that one homie )
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Cooperate weekend 🏝️
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Need some good voice Ai ideas Drop 👇
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101B MileStone Achieved..
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Mohit Goyal (Harness arc) retweeted
Our #neatHack submission Everyone goes on about tokenmaxxxing. But what about when you are on a budget? There have been countless incidents where devs have gone at par with their expected credits budget. That's why we introduce: Tally, the CFO for your AI Agents. Just do: npm install -g tally-agents, and it runs in your terminal. You can do it yourself or just let your agent handle the hassle 👀 Built with: @arithecfo: helped frame Tally’s business around cost per verified task, making agent spending and potential savings measurable. Tally reads the monthly budget, records actual spend by model tier, and sends attempt reports to Ari for cost analysis and hypothetical model comparisons. @EntireHQ: to track our work while building Tally and save checkpoints of important changes. Tally also uses Entire’s code graph to understand which files a task may affect. If a worker hits its budget limit, checkpoint information helps the next worker continue from where it stopped. When the graph finds no matches, Tally searches the repository instead. @neatlogs: to track which model Tally chose, how much it spent, and whether each task succeeded. Tally uses those past results to help choose models for future tasks. The traces also helped us debug workers starting or stopping incorrectly. Our latest parallel run automatically recorded both Tally’s decisions and the workers’ activity. @useOrchestrator: to coordinate the coding agents building Tally, each in its own branch and workspace. Tally also uses it to launch workers on selected models, monitor their token usage, and stop them at budget limits. @crustdata: We used CrustData to research potential customers for Tally, looking for software companies with 20–500 employees. We reviewed 10 returned companies and kept six as possible prospects for the business plan. links in comments :) xitter.cf/bettersayAJ/status/210…
building agents is easy getting them to production is hard today we’re launching @neatlogs to fix that
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Mohit Goyal (Harness arc) retweeted
One of SUGAR Cosmetics’ ads has been live for 1,605 days. Nobody keeps an ad on for four years unless it works. Your rivals have ads like that right now, and you probably can’t name them. Competitor research is still manual: search each brand, skim their YouTube, scroll the Ads Transparency Center, paste screenshots into a deck. A day of work, stale by Friday, and nobody can say where a number came from. So I built Outfox. It reads Google Search, the Ads Transparency Center, YouTube, Trends and News through SerpApi, keeps every result with its source link, and tags the hook each one uses. Then you just ask: “Compare SUGAR, Minimalist and Mamaearth on discount hooks.” Every number in the answer opens the public page it came from. No invented ad spend. No guessed reach. If a source fails, you see a gap, never a zero. Watch it in 65 seconds 👇
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Mohit Goyal (Harness arc) retweeted
Our #neatHack submission Would you give your coding agent full access? Neither would we. Solo License makes the agent EARN it: verified runs unlock autonomy, force-pushes get denied, over-edits get sent back, and every decision has a receipt. Try it live 👇 solo-license-dashboard.verce… Built with @arithecfo @EntireHQ @neatlogs thanks @akshay_hangloo for crazy guidance xitter.cf/bettersayAJ/status/210…
building agents is easy getting them to production is hard today we’re launching @neatlogs to fix that
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One of SUGAR Cosmetics’ ads has been live for 1,605 days. Nobody keeps an ad on for four years unless it works. Your rivals have ads like that right now, and you probably can’t name them. Competitor research is still manual: search each brand, skim their YouTube, scroll the Ads Transparency Center, paste screenshots into a deck. A day of work, stale by Friday, and nobody can say where a number came from. So I built Outfox. It reads Google Search, the Ads Transparency Center, YouTube, Trends and News through SerpApi, keeps every result with its source link, and tags the hook each one uses. Then you just ask: “Compare SUGAR, Minimalist and Mamaearth on discount hooks.” Every number in the answer opens the public page it came from. No invented ad spend. No guessed reach. If a source fails, you see a gap, never a zero. Watch it in 65 seconds 👇
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What’s inside: > a Feed of every rival’s newest ads, videos and news > a profile per brand, with how many days each ad has run > Signals: which hooks your whole market leans on boards to save and share findings > Ask, a chat that answers only from stored evidence
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Outfox only says what public data can prove. Ad spend, reach and conversions aren’t public, so it marks them unavailable instead of guessing. Which rival would you track first?
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Mohit Goyal (Harness arc) retweeted
Guyz giveaways take part in this and get 100$ credits Ps: engage
no observability tools were harmed in the making of this video
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Tried cooking something in this mid week of mid sem . let's see how it goes.
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bas itna cook karna h
can i CoOk✋ drumrollssss 🥁🥁🥁 1000+ okay fine, had to post my intro video😭 jaldi jaldi dekho → ziddi.pro/
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Rejection Streak : 80x
Rejection streak started🤧🥀
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Mohit Goyal (Harness arc) retweeted
Thanks @jetbrains @rustrover Nice to be part of interview with Sofia .......looking Forward to use Rust Rover ...
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