Mohak Sharma
@mohak__sharma
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co-founder and ceo @honeyhiveai - hiring!
NYC
Joined June 2020
I'm excited to share we've raised $7.4M Seed from @gkm1 at @InsightPartners, along with @petesoder at @ZeroPrimeVC, @antgoldbloom at @AIXVentures, @flo at @468Capital, and others to bring evals & observability to AI agents!. We're also going GA today! 🧵
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RT @honeyhiveai: Introducing Role-Based Access Control (RBAC) in @HoneyHiveAI!. Built in partnership with our largest financial services an….
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RT @honeyhiveai: Chasing benchmark leaderboards is the easiest way to build an AI product that fails in the real world. Most teams waste 9….
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Particularly proud to share that a major Fortune 100 enterprise is already logging their PHI data to our HIPAA-compliant cloud. Learn more about our security policies here:
We're excited to announce that HoneyHive has officially achieved SOC 2 Type II, GDPR, and HIPAA compliance!. Every LLM interaction today—from user prompts to contextual retrieval or tool-use data—contains potential PII and PHI, which is why we've built our platform with
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Any product designers / people with good UX taste up for grading some UX mockups for a vibe coding eval?. 💴 Paid opportunity!.
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Most recommendation systems just show you more of what you already like. The trouble is, we can't always describe the new things we might enjoy. This technique works differently. It learns what you like and don't like, then helps you discover truly new content.
Traditional vector search systems often struggle with nuanced user preferences that are difficult to articulate. Our latest collaboration with @qdrant_engine showcases an iterative optimization technique that dynamically adapts search results based on user preferences.
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Evals are a means to an end (better product), not the end itself.
Product evals are misunderstood. Many teams think that adding another tool, metric, or llm-as-judge will solve all their problems and save their product. But that just dodges the hard truth and avoids the real work. Here's how to fix your process instead.
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RT @jobergum: Because we struggle even with a single agent, never mind the exponential complexity of n agents.
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Funny how this is being resurfaced 4yrs later after @ds3638 built the original version at Microsoft:
Also released today is Codex CLI — an open-source lightweight coding agent that runs in your terminal: This is the first of a series of tools we'll be releasing over upcoming months, which we think show the future of programming.
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At @honeyhiveai, we call them the "YOLO to prod" people. Too easy to ship a half-baked app when no one knows how to really do ML. The core issue is SWE doesn't prep you for ML's experimentation culture. ML is more science than code—and that's what most people are getting wrong.
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RT @qdrant_engine: 🔎 See inside your RAG pipeline with Qdrant + @honeyhiveai . You can now trace every step of retrieval and generation: em….
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RT @honeyhiveai: By popular demand, we’re excited to introduce an integration with @qdrant_engine!. Retrieval powers most AI apps today, bu….
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RT @lancedb: Hard to debug & optimize your RAG apps in production? 🤔 .LanceDB <> @honeyhiveai integration gives you the visibility you nee….
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And to learn more about Insight’s POV, check out this blog post:
We’re thrilled to lead HoneyHive's seed round and partner with Mohak Sharma and Dhruv Singh to improve reliability for multi-agent systems!. Read more about our thesis behind the investment here:
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Not to mention the vendor lock-in (imagine not being able to compare o3 vs sonnet 3.7?!). Luckily @honeyhiveai exists :).
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This doesn’t really work for most real-world LLM apps which are typically multi-step, compound AI systems. Issues can stem from other parts of your system too (eg: retrieval, query decomposition, etc.). This means you need to eval each step individually as well as the entire.
Introducing the Evals API. You can now programmatically define tests, automate evaluation runs, and quickly iterate on prompts. Evals are still available in the dashboard—and now through the API, so you can integrate them anywhere in your workflow.
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RT @MVP_Ventures: Excited to back the @honeyhiveai team as they solve one of the most under-appreciated challenges in AI: making systems re….
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RT @DANGio25: Evals are the critical building block to achieve magical production ready AI products. HoneyHive powers that - congrats on th….
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To learn more about our announcement, check out our blog post:
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We're excited to go GA today after seeing incredibly strong beta adoption over the past 18 months from both AI-native startups and Fortune 100 enterprises. The team and I can't wait to see what you'll build with HoneyHive!.
1/ We’re thrilled to announce we've raised $7.4M in total funding led by @InsightPartners along with @ZeroPrimeVC, @AIXVenturesHQ, @468Capital, and @FirestreakVC to bring evals and observability to AI agents. HoneyHive Cloud is also generally available starting today.
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