Petal, open-source CoScreen/ScreenHero finally free
Hi HN!I’m Jason Thomas, one of the founders of CoScreen.As some of you may know, Datadog sunset the CoScreen service on July 31st.Four of us from the original CoScreen team have spent the last couple months building a free and open source pair programming tool inspired by those that came before us: It’s called Petal (https://github.com/kiruna-labs/petal)Some history first, because when we started CoScreen we had hoped to break the sad and established pattern of this industry.Screenhero was an exceptional tool that a lot of us loved for pairing. Multiple cursors, low latency, you could click your partner’s editor, and just start typing. It felt like magic while we had it. Then Slack bought it in 2015, and shut down the standalone app in December 2017.When Till, Max, and I started CoScreen, we were aware of this history, and wanted to build an even more magical collaboration and screen sharing experience: the entire concept was “throw a window from your laptop up on someone else’s screen, and start collaborating with zero friction.”Datadog acquired us in 2022, and thus began the great adventure of adapting our real-time technology into Datadog products, while also continue developing CoScreen as a standalone tool from the Datadog ecosystem. Datadog invested in the product for years and we’re thankful for that. But CoScreen itself proved, like so many other screen sharing tool acquisitions, to be too tangential from the customer base and core mission. We instead built other communication features in Datadog, the integrations with other video platforms, and also its wildly successful MCP server. We weren't the only ones. Around was bought by Miro and shut down in March 2025. Multi (formerly Remotion) was acqui-hired by OpenAI in 2024 and shut down about a month later. It's nearly always the same story: a small team builds something hard that people love, a platform buys it for the team or the feature, and the standalone product doesn't
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
An open-source, frictionless pair programming tool built by the original CoScreen team to revive a beloved but discontinued collaboration experience.
The pain
The gap
Build angle
Strengths
- Founders have deep domain knowledge and prior success with CoScreen.
- Clear user pain and demand for frictionless pair programming tools.
- Open-source model can attract community support and adoption.
Risks
- Unclear monetization strategy and user willingness to pay.
- History of similar tools being acquired and discontinued.
- Potential difficulty in scaling user base beyond niche developers.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 80
- Solution gap
- 75
- Willingness to pay
- 50
- Buildability
- 75
The pain of lacking good, frictionless pair programming tools is clear, and an open-source alternative fills a gap, but monetization and user willingness to pay are uncertain.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The idea addresses a real problem with a differentiated solution, but the market size and monetization strategy are unclear, limiting its immediate value capture potential.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The product fits a niche developer need with a clear technical edge, but the competitive landscape and user acquisition challenges reduce its overall attractiveness.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The team has relevant experience and the product solves a known problem, yet the history of similar tools being acquired and shut down suggests market risk.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The founders have domain expertise and the product solves a real pain, but the lack of a clear business model and previous shutdowns of similar tools raise concerns.
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
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