Kino AI (YC S23) – AI media manager and video editor
Hi HN! I'm Luke from Kino (https://kino.ai). Kino lets you make great videos with your agents and your teammates. Storage of footage and assets, a rich video editor, and review links for teammates are all bundled into one. Your colleagues and Codex / Claude Code can work on the same timeline with review notes and footage all in one place. See a basic overview at https://kino.ai/walkthrough-video and our launch video (made entirely with Kino!) at https://kino.ai/launch-videoOpus 5.5 can edit videos and make great motion graphics now. But with a raw agent harness, you need to describe tiny edits either in words or in quick one-off UIs, you can’t refer to footage shared across your devices, and feedback from teammates comes as screenshots on Slack.Kino is code-first. All editing, all motion graphics, all dragging of timeline items around, all teammate comments, and all 3D scenes are expressed as code, built on Remotion. This was hard to get right, as we found ourselves building a what-you-see-is-what-you-get editor for React, which we then turned into a video editor. For our launch video I told Claude-with-Kino to find a macOS Leopard install disk and reskin our whole video to match.Kino is also fully collaborative and real-time, so you can see your teammates' cursors and exactly which part of the timeline they’re currently watching. For our launch video I had 5+ agents editing the timeline concurrently while my friend Donald had his swarm working a few minutes further down. Donald had one agent rotoscoping to make our visual effects work, I had an LLM color grading all media via our LUT support, and we both made tiny tweaks to the timeline along the way.To make this code-first editor work with video footage, everything gets transcribed, embedded, described, and face-detected. You can scrub your footage locally after you drag it in, but we upload a low-res proxy so your teammates and cloud inference can see it. Thi
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
Kino AI is a collaborative, code-first video editor that integrates AI agents and teammates in real-time to streamline video production workflows.
The pain
The gap
Build angle
Strengths
- Clear pain point in video collaboration and editing workflows
- Innovative code-first approach enabling precise, programmable edits
- Strong AI integration for automating complex editing tasks
- Real-time multi-user collaboration with shared timelines and comments
Risks
- High technical complexity may slow development and scaling
- User adoption may be limited by learning curve of code-driven editing
- Competition from established video editors adding collaboration features
- Unclear monetization strategy and willingness to pay at scale
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
- 75
- Willingness to pay
- 70
- Buildability
- 80
The pain of disjointed video collaboration and editing is clear, Kino fills a gap with a code-first collaborative editor, customers likely pay for streamlined workflows, and the technical complexity is high but manageable.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The idea targets a real pain with a differentiated solution, but pricing and customer acquisition details are unclear, limiting confidence in immediate growth potential.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Kino addresses a niche in video editing collaboration with a unique code-driven approach, showing good product-market fit potential but faces competition from established tools.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The concept is innovative and technically challenging, with a clear user base, but risks include adoption barriers and the complexity of real-time collaboration at scale.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Kino fits YC’s focus on developer tools and AI, has a strong technical foundation, and solves a real problem for teams, though market size and monetization need validation.
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!
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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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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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