EvalLens
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Lens your next unicorn πŸ¦„ Your reviewers fade by deck 40. We don't. Batch-read, ranked, receipts attached, the final call stays yours!
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A deck gets two minutes and a yes-or-no. Almost never the why. That silence is where founders and investors lose each other. EvalLens is the read in between.

We ran it live for the first time on Friday, at Startup A2 Hub in Nha Trang. Five teams, six dimensions. An AI panel scored every deck. Then the room made the call together. AI prepares the analysis. People decide.

We booked two hours. Everyone stayed three! A venture fund signed an LOI to test us on their deal flow. One founder started rebuilding their pitch for pre-seed. Both sides, one table. (Every team also took home a commemorative Bitcoin and Ethereum souvenir coin. A keepsake, not the real thing.)

Your turn. Send your deck. We run the same six-dimension read and show you where it lands and where it leaks. Your first run's on us!
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When AI agents run terminal commands autonomously, local workstations become an unmonitored supply chain vector.

SafeDep addresses this with PMG, an open-source Go proxy. It intercepts npm, pnpm, and pip commands to enforce dependency cooldowns and check threat APIs before packages hit disk.

Founder Abhisek Datta hits a real pain point, but local proxy wrappers carry inherent friction: resolution latency annoys developers, and the threat feeds can be built natively by registries.

Backed by $350K from Grayscale Ventures and Transpose Platform Management, SafeDep has 450 GitHub stars but zero monetization. A free terminal wrapper is handy, but turning it into a paid security platform before registries replicate it is the hard part.

🎯 49/100 Β· SWOT, competitors & risks β†’
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EvalLens Γ— Crypto Executives: partnership signed!

Big one for us. Crypto Executives - the largest crypto C-level community in the world - becomes an official distribution partner of EvalLens.

If you don't know CE yet: advisory that takes founders to fundraise-ready, a deal flow board, demo days, a marketplace with 300+ KOLs. A network where hundreds of decks a year change hands - and where speed decides.

Huge congrats to the CE team, our team and community! Thrilled about this one! Here's to a juicy partnership 🀟

Meet Crypto Executives:

πŸ“²Crypto Executives X
πŸ›œ cryptoexecutives.io
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Chaining separate databases for vector search, full-text, and ACID transactions is the default friction when building AI agent memory. Datalevin attempts to solve this by consolidating Datalog queries, search, and vectors into a single embedded engine.

Creator Huahai Yang spent 6 years reaching 1.0, stitching together open-source primitives like usearch, llama.cpp, and DLMDBβ€”a custom fork of LMDBβ€”into a unified data substrate.

The friction-free integration is tempting, but the risk is structural. Maintaining a custom LMDB fork as a solo maintainer creates steep overhead, while deep ties to the Clojure ecosystem cap adoption before broader dev stacks notice it.

Is an integrated substrate worth building on if its foundation depends on a custom fork?

Full analysis here β†’