Skillsync (YC W26) – AI chat sessions made portable across agents
Hey HN, we're Nars & Nishant, founders of Skillsync (https://skillsync.com)Skillsync lets you move your AI chats across every coding agent. Most of our work exists as conversations, which are currently scattered across our agents. Though stored locally, these conversations use different formats. This is annoying because you cannot simply switch between agents without starting over. We get locked into a single provider and their agent as we invest in skills and memories over time. Skillsync acts as a universal converter. It moves the entire session, including all the messages, reasoning and tool calls so you can pick up right where you left off.Skillsync collects all your sessions in one place and makes them searchable. It breaks down what each session is carrying, including which loaded skills the agent is actually using, making stale context easy to spot. You can also create shared workspaces to sync sessions across your team. You can build your own closed loop systems. Everything runs locally except when you share to workspaces.The core is an open-source Rust engine called txcript (https://github.com/skillsynchq/txcript). It translates a session from one agent's on-disk format into another's, mapping conversation, reasoning, and tool history. Think ffmpeg or pandoc, but for agent sessions.On top of that engine is a local-first desktop app. Your agent sessions are normally scattered across different tools' folders in formats you'd never read by hand; the app surfaces them in one place with a UI that makes them actually readable, the conversation, the reasoning, and the tool calls, so you can revisit what happened, move a session into another agent, or share it with a teammate.Skills and memory are stored as portable, human-readable markdown you own, and exposed to any agent over MCP for search and selective retrieval.Sessions and translation run locally on your machine. The one thing that leaves is what you
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
A session translator and local-first hub for AI coding agents that lets teams move conversations between tools without losing context.
The pain
The gap
Build angle
Strengths
- Founders have already built the core engine (txcript) in open-source, reducing execution risk.
- Local-first architecture with optional sharing means users own their data and can adopt without trust concerns.
- Portable markdown format for skills and memory is human-readable and not proprietary.
- Clear technical positioning as a format converter, similar to ffmpeg or pandoc, which is easy to explain.
- Team sharing feature addresses a real collaboration gap that single-agent tools do not solve.
- Timing is good because the agent market is fragmenting and users are starting to feel lock-in pain.
Risks
- Willingness to pay is uncertain because users may not switch agents frequently enough to justify a subscription. The value is highest for power users and teams, which is a smaller market.
- Agent vendors may change their session formats or add encryption to prevent third-party access, breaking the translator.
- The product is a tool for a tool, which means adoption depends on adoption of multiple agents. If one agent dominates, the problem goes away.
- Buildability is moderate because translating between agent formats requires reverse-engineering each vendor's session structure and keeping up with changes.
- Monetization is unclear. Charging per session, per user, or per team all have friction. Free tier with paid features may cannibalize the core value.
- The open-source engine may be copied by a larger vendor (e.g., Anthropic, GitHub) as a feature, eliminating the moat.
- Team sharing and workspaces add complexity and may not be the primary use case. Most users may just want to switch agents for personal projects.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 78
- Solution gap
- 75
- Willingness to pay
- 68
- Buildability
- 68
Real problem of fragmented agent sessions with a technically sound solution, but willingness to pay and solo buildability are moderate constraints.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Solves a specific workflow pain for a growing user base, but the market size depends on how many people actually switch between agents rather than pick one and stay.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Strong technical execution and clear positioning as infrastructure, but the value capture is unclear because users may not want to pay for portability if they are already locked in.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Addresses a real coordination problem in teams using multiple agents, but the TAM is limited to teams that actively use multiple coding agents and care about session continuity.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Solid infrastructure play with a technical moat via the open-source engine, but needs to prove that agent switching is common enough to sustain a business.
Five lenses, one composite. How scoring works
The angle
No market research recorded for this idea yet.
Semble – Code search for agents that uses 98% fewer tokens than grep
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.Main features:- Token-efficient: 98% fewer tokens than grep+read- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode- Zero config: no API keys, no GPU, no external servicesInstall in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" sembleOr check our README for other installation instructions, benchmarks, and methodology:Semble: https://github.com/MinishLab/sembleBenchmarks: https://github.com/MinishLab/semble/tree/main/benchmarksModel: https://huggingface.co/minishlab/potion-code-16MLet us know if you have any feedback or questions!
AI
Postgres extension for BM25 relevance-ranked full-text search
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
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GlycemicGPT – Open-source AI-powered diabetes management
I'm a Type 1 diabetic and software engineer. Last year I went months between endocrinologists with no clinician reviewing my data. I'm an engineer, so I built the tool I needed — and now I'm open sourcing it. GlycemicGPT is a self-hosted platform that connects continuous glucose monitors, insulin pumps, and existing Nightscout instances to an AI analysis layer running on your own infrastructure. Data sources:Dexcom G7 (cloud API) Tandem t:slim X2 and Mobi pumps (direct BLE) Nightscout (point it at your existing instance and you're running in minutes)What the AI layer does:Daily briefs summarizing overnight and 24-hour patterns Meal response analysis Conversational chat with RAG-backed clinical knowledge Predictive alerting with configurable thresholds and caregiver escalationImportant: this is monitoring and analysis only. GlycemicGPT does not deliver insulin, does not control your pump, and is not a closed-loop system. It reads your data and gives you insight on top of it. Your clinical decisions stay between you and your care team. Architecture:Self-hosted via Docker or K8S — the GlycemicGPT stack runs entirely on your hardware BYOAI — bring your own AI provider. Use Ollama for fully local operation (no data leaves your hardware), or point it at Claude, OpenAI, or any OpenAI-compatible endpoint if you prefer a hosted model. Data flows directly from your instance to the provider you choose; nothing is routed through any centralized service operated by the project. GPL-3.0, no subscriptions, no vendor lock-inStack:Backend API: FastAPI, Python 3.12, PostgreSQL 16, Redis 7 Web Dashboard: Next.js 15, React 19, Tailwind CSS, shadcn/ui AI Sidecar: TypeScript, Express, multi-provider proxy Android App: Kotlin, Jetpack Compose, BLE Wear OS: Kotlin, Wear Compose, Watch Face Push API Plugin SDK: Kotlin interfaces, capability-based, sandboxedLooking for contributors — especially folks with BLE/Android experience or anyone in the diabetes tech spa
AI
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