Durable Actors – OSS Durable Objects with configurable compute
Hi HN, we're Thomas and Olivier from Terse (https://www.useterse.ai/) We've built Durable Actors, an open-source alternative to Cloudflare's Durable Objects.A Durable Object/Actor is a tiny server that handles one request at a time and has its own SQLite database. There's exactly one of each in the world and it is addressed by name.This is the perfect primitive for deploying multiplayer agents. Each agent can have its own Durable Actor, and each user can connect to that Actor via websocket. This is fully horizontally scalable. Your users can deploy and share agents at will without putting pressure on a central DB or websocket server.Durable Actors are also great for coordinating agents within a system. Since only one request is handled at a time, you can protect critical data such as a CRM and allow multiple agents to run concurrently without worrying about data races.The only alternative to this is Cloudflare's Durable Objects. However, there is extreme lock in (they pull you into D1, R2 + workers as well) and it wasn't originally built for agentic workfloads when it was released 5 years ago.Some notable projects built on Durable Objects include RampInspect, OpenInspect as well as the multiplayer frameworks Liveblocks and PartyKit. You can now build these kinds of projects on Durable Actors.Durable Actors is a version of DO that is built for concurrent agentic workloads. It is fully open source (MIT License) and includes a helm chart for you to easily self-host.Some key features:- Configurable compute: Specify CPU, RAM, data residency, idle-timeouts all in a decorator- No outer worker: We generate a type-safe client that you can just plug into your existing tech stack.- (coming soon, like today) Export SQLite table via CLI + MCP for exposing OLTP logs to your agent to help you debug.And our Performance Numbers (all p95):- Durable write: 85.6ms- Stateful Read (data in sqlite): 2.14ms- Actor Warm up: 334msHere is a li
FL score
out of 100
Verdict
high confidence
Competition
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Trend
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An open-source, configurable alternative to Cloudflare Durable Objects designed for scalable, stateful, concurrent agent workloads.
The pain
The gap
Build angle
Strengths
- Open-source with permissive MIT license
- Configurable compute resources per actor
- Type-safe client integration without outer worker
- Performance metrics competitive with proprietary alternatives
Risks
- Niche technical audience limits market size
- Monetization strategy unclear without cloud service
- Potential complexity in self-hosting and operational overhead
- Competition from established Cloudflare ecosystem and other serverless platforms
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 85
- Solution gap
- 80
- Willingness to pay
- 70
- Buildability
- 75
The project addresses a clear pain point of vendor lock-in and concurrency issues in stateful serverless actors, with a viable open-source alternative that users can self-host.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Durable Actors solves a specific technical problem with a differentiated open-source solution, but the market willingness and monetization path are somewhat uncertain.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The idea targets a niche developer audience with a real technical need, but the complexity and adoption barriers may limit rapid growth and broad appeal.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The solution fits a defined problem and has technical feasibility, though the competitive landscape and customer acquisition require careful navigation.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Durable Actors has a strong technical foundation and addresses a pain felt by developers tied to Cloudflare’s lock-in, but product-market fit and monetization remain to be proven.
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
AI
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
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