Waymark – A workflow engine that compiles jobs instead of replaying
Hey everyone! @mozgiii and I have been working on Waymark for the better part of the last year. Waymark’s an open source durable execution library with similar functionality to Temporal or DBOS.I fell in love with durable execution a few years ago, after working at a company with a heavy pubsub architecture. In comparison to job orchestrators that force a certain task design or special syntax, many of these durable libs lets you write business logic in regular code: `asyncio.gather` to parallelize tasks, `for` loops, conditionals, and let the runtime figure out how to run them efficiently across a cluster of nodes. But most of the current solutions require a heavy broker, runtime hacks to try to ensure deterministic workflow logic, and feel like you have to learn a whole new ecosystem just to get started.A few unique design decisions in waymark:— Write jobs in normal code (right now Python but JS coming soon). Instead of running control flow as-is, which requires booting up an interpreter to replay past state, we’ll just compile your code directly. We walk your AST and map your logic to our stackless VM bytecode. We support variables, conditionals, await, sleep, helper functions, loops... basically what you’d expect in workflow logic.— Since we’re compiling a language subset, we can proactively flag errors in your workflows locally instead of when they run in production.— It only requires Postgres as the task broker. We’ve spent a lot of time tuning our design to play nicely with its strengths and limitations; our current benchmarks show around 1500 workflows/second but we still have some more optimization work to do here.— Actions plug-and-play with FastAPI’s dependency injection syntax. So if you already have an ecosystem based on typehinting/DI, it should fit like a glove.It allows for parallelization of workflows across one machine or a whole cluster. The bulk of the work per node is done by our central Rust core - which distributes workflows to proper
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
Waymark is an open source workflow engine that compiles jobs into bytecode to simplify and speed up durable execution without heavy brokers.
The pain
The gap
Build angle
Strengths
- Innovative compilation approach reduces runtime complexity
- Uses standard Postgres as broker, lowering infrastructure needs
- Supports normal code syntax, easing developer adoption
- Open source with multi-language support planned
Risks
- Strong competition from established players like Temporal
- Unclear monetization and customer acquisition strategy
- Potential technical challenges in scaling and optimization
- Market may prefer fully managed solutions over open source libraries
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 pain of complex, heavyweight workflow orchestration is clear, and Waymark offers a novel approach with compiling workflows, but market willingness and ease of solo build are moderate.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The idea addresses a real pain with simpler workflow orchestration and has a clear value proposition, but customer acquisition and monetization paths are not fully defined.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Waymark fits a niche in workflow engines by reducing complexity and dependencies, showing good product-market fit potential though competitive differentiation is moderate.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The solution is technically sound and innovative, but the market size and ease of scaling customer adoption remain uncertain.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Waymark tackles a technical bottleneck in workflow orchestration with a developer-friendly approach, but the business model and go-to-market strategy need more clarity.
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
Consus (YC F24) – LLM gateway for companies handling sensitive data
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.Demo: https://youtu.be/6MYxJe6P4oYAerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Clau
AI
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Waymark – A workflow engine that compiles jobs instead of replaying”.