Top Tweets for #hackhydra
Day 229 of 2026 building!
Just completed the finale for Tracewood for #HackHydra!
Integrated the official @hydra_db Cloud SDK (v2 API) to turn scattered AI coding agent logs into an interconnected 3D WebGL context graph!
- 10 AI Agent Harnesses Detected (Claude Code, Cursor, Copilot, Windsurf, Aider, etc.)
- Architectural Constraint Invalidation Engine (VIOLATES graph edges)
- Cross-Repo Implicit API Dependency Network (EXPOSES / CONSUMES)
- Dual-Store Context Fusion + POST /feedback signal tuning
Submitted Errata for #HackHydra
Errata: an append-only belief graph for agent memory.
→ keeps the evolution of facts instead of overwriting old beliefs
→ lets agents reason about what changed, what superseded it, and what the latest state is
Built on @hydra_db .
I shipped Cortex
An organizational second brain powered by HydraDB turning scattered company knowledge into connected memory, reasoning & AI agents that work where teams already do
#HackHydra
HydraDB is by far my favourite in terms of performance snd quality when it comes to RAG and such use cases.
Share your feedbacks and we’ll make sure to bring personal memory to ur canvas to make it more productive and accessible.
@contextkingceo @iHarnoorSingh #HackHydra
Repo + demo video:
https://t.co/dnRKj46jUQ
docker pull https://t.co/YXcaskuEms
Built by @theyclonedsam
#HackHydra #HydraDB #buildinpublic
Introducing Tavik.
It answers one question: who can reach my production, and how?
Point it at any public repo, It maps every package, asks npm who can publish each one, and proves every route hop by hop.
Built on @hydra_db for #HackHydra
https://t.co/NPcXRxSCyA
HydraDNA vs BM25 on LoCoMo (300 samples, same judge, 0 failures): wins every retrieval metric. Causal edges beat lexical RAG on multi-hop + temporal. 📊 @hydra_db #HackHydra
Nudge your agent into asking WHY with HydraDNA
https://t.co/sU7R3I8E5T
@abhirupvg @AbhinavXJ
Dogfooding.
Watch me build HydraDNA’s landing page with HydraDNA 🤝
Your causal dependency graph grows with your project, while retrieval + agent understanding takes 46× fewer tokens than a vanilla setup.
Find your context’s DNA.
@hydra_db #HackHydra
https://t.co/sU7R3I8E5T
AI agents pick which tests to run using a map of your code. Nobody checked the map. We did... it misses the guarding test half the time. So we built the seatbelt.
🔺Built on @hydra_db for #HackHydra
ft. @jayaticore
https://t.co/9wBEfeWnOL
What happens when a critical employee, source, or knowledge system disappears?
BLACKOUT for #HackHydra a graphnative enterprise knowledge resilience system.
People → Sources → Claims → Decisions → Systems
Then simulate node removal and calculate which knowledge paths brek
Built TESSERA: 511,962 enterprise docs across 9 sources, one graph.
Instead of guessing identity from embeddings, it traces shared neighborhoods in @hydra_db
91% accuracy on EnterpriseRAG-Bench. Graph-native ontology.
@iHarnoorSingh @contextkingceo @abhirupvg
#HackHydra
⏬️
errata is done and live for #HackHydra.
an append-only belief graph for agent memory on @hydra_db. when a fact changes, the old value stays in the graph and the new one supersedes it with an edge.
demo and full eval results: https://t.co/GA7h4SNjWG
building Errata: an append-only belief graph for AI agents using @hydra_db for #HackHydra .
instead of overwriting a memory when it changes, Errata keeps the old belief and records the correction as a new edge.
ran our latest LongMemEval eval on 150 questions × 3 seeds.
- overall: 60.0%
- multi-session: 72.1%
- knowledge-update: 95.8%
- all 450 runs: 66.7%
- latest improvement came from moving arithmetic out of the LLM and computing it directly from the graph.
slowly slowly, we're getting there💪
Built Imprint on @hydra_db for #HackHydra: persistent memory for AI coding agents, backed by a real graph. Facts become nodes; contradictions become SUPERSEDES edges instead of silent overwrites. @iHarnoorSingh
🔗 https://t.co/bf17QbtmlE
💻 https://t.co/zPuoH6LAZF
Dogfooding.
Watch me build HydraDNA’s landing page with HydraDNA 🤝
Your causal dependency graph grows with your project, while retrieval + agent understanding takes 46× fewer tokens than a vanilla setup.
Find your context’s DNA.
@hydra_db #HackHydra
https://t.co/sU7R3I8E5T
still building Joel for #HackHydra with @hydra_db
took a pause from the debugging to make this. now back to building.
https://t.co/boUkvX98Km
been building Joel this week for #HackHydra with
@hydra_db — a self-hosted company brain, on the enterprise context track.
it's an interesting hard problem to solve. having a lot of fun working on how companies actually remember things across slack, docs, tickets… and whether a graph can hold that together better than search alone.
https://t.co/z3d8q3Kbwv
Day 6 of #HackHydra: Seismic is no longer just an idea. It now works end to end.
Seismic gives coding agents graph-native change intelligence before they edit code.
Today I finished:
• Python MCP server with 7 stateless tools
• Repository indexing into HydraDB over Bolt
• Exact blast-radius analysis with HydraDB algo.SSpaths
• Evidence paths for every impact claim
• Explicit abstention when the graph cannot prove a dependency
• React dashboard for exploring affected functions, tests and entry points
• A complete Remotion demo with Murf narration
• Public GitHub repo and judge-ready docs
Verified on a real local HydraDB instance:
• 16/16 Seismic tests passing
• 10/10 exact-path smoke cases
• 2/2 abstention checks
• 25/25 stress targets retrieved from a 5,000-artifact graph
I also reran HydraMind, my Track 03 project: 48/48 tests passing.
Two solo projects, one goal: give coding agents durable graph context they can actually defend.
Repo: https://t.co/PSy8YrmBTz
Built with @hydra_db for Track 02B.
46× fewer tokens.
2-0 on accuracy, retrieval that doesn't hallucinate.
Flat-window AI can't see the cause; causal memory walks straight to WHY.
@hydra_db is goated out of the box, but HydraDNA makes it hit harder for your harness. #HackHydra
demo: soon
FINALLY GAVE A NAME TO IT!!
HydraDNA for #HackHydra - context with DNA that answers WHY. Built on @hydra_db.
Give your agent's context a DNA. Answer "WHY" instead of just retrieval - every decision a node, auto-linked into causal chains.
Dogfooding it daily.
Benchmarks soon.
Day 5 of #HackHydra and I’ve started building my second solo project: Seismic 🌋
Seismic is a graph-native change intelligence layer for coding agents, built on @hydra_db for Track 02B.
Before an agent edits a file or function, Seismic will answer:
• What depends on this code?
• Which callers, tests and entrypoints could be affected?
• What is the exact dependency path?
• Has this change path caused incidents before?
Instead of retrieving code because it looks semantically similar, Seismic models files, symbols, calls, imports, tests, packages and incidents as a real graph. It then uses HydraDB’s native bounded path traversal to calculate the blast radius.
Today I locked the architecture, created the independent repository, started the local HydraDB node without Docker, verified the real Bolt connection and began the Python AST plus immutable snapshot ingestion layer.
The goal is to let coding agents know what a change can break before they make it and make every result explainable with an exact graph path.
HydraMind is my Track 03 project. Seismic is a completely separate Track 02 build. Two distinct graph problems in one very intense hackathon week.
Building in public. More tomorrow. ⚡
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