BitBoard (YC P25) – Analytics Workspace for Agents: VALIDATE, 72/100 | Ideas Lab

A workspace that lets humans and AI agents collaborate on data analysis with shared dashboards, provenance tracking, and semantic models instead of treating analysis as disposable chat outputs.

The pain

Companies have data scattered across systems with no single source of truth. When they use AI for analysis, results disappear into chat history. When they use legacy BI tools, agents can't actually control or contribute to reports. Teams can't verify agent work or share discoveries across the organization.

The gap

Legacy BI tools (Tableau, Looker) weren't designed for AI agents as first-class users. Chat-based AI tools (Claude, ChatGPT) treat analysis as ephemeral conversation. Nothing lets humans and agents work on the same canonical data objects with mutual accountability and visibility.

Build angle

Start with teams that already have AI agents doing analysis work (data teams, operations, finance). Let them connect their existing agents to BitBoard and immediately see: (1) persistent dashboards instead of lost chat threads, (2) audit trails showing what the agent did and why, (3) human-agent collaboration on the same metrics. Charge per agent connection or per dashboard. Expand to semantic layer features once adoption proves the core value.

Strengths

Founders pivoted from healthcare agents to this after customers pulled them toward data problems, shows real demand signal, not theoretical
YC backing and clear positioning against two large categories (legacy BI + AI chat) rather than trying to be everything
Solves a concrete problem: agent outputs today are invisible and unverifiable, making them unusable in regulated/high-stakes contexts
Semantic layer angle (canonical entities, measures, provenance) is defensible and creates switching costs once embedded
Demo exists and shows working product, not vaporware

Risks

Market timing unclear, most companies haven't deployed agents for analysis yet, so TAM may be smaller than it appears. Could be 2-3 years early
Semantic layer is table stakes but not differentiated, Cube, dbt, Atlan all do this. BitBoard's edge is agent-specific UX, which is unproven at scale
Sales motion is complex: need to land with teams that have both (a) AI agents already in use and (b) budget for new tooling. Narrow wedge
Integration risk: agents from different vendors (Claude, OpenAI, custom) may have different APIs and capabilities. Becomes a support burden
Legacy BI vendors (Tableau, Looker, Microsoft) will copy the agent collaboration features once they see traction. Hard to defend long-term
Pricing model not stated, unclear if this is a $500/month tool or $50k/year. Affects GTM viability

Fly Labs Method

Is the pain real, is there a gap, is it the right time, can one person build it.

72

Value Equation

Dream outcome and how likely it feels, against the time and effort it costs.

68

One-Person Business

Curiosity pull, identity fit, and a path from free value to paid for a solo creator.

75

Viral Frameworks

Hook strength, shareability, and how cheaply it can be tested.

70

Builder Lens

Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.

78

Five lenses, one composite. How scoring works

The angle

Start with teams that already have AI agents doing analysis work (data teams, operations, finance). Let them connect their existing agents to BitBoard and immediately see: (1) persistent dashboards instead of lost chat threads, (2) audit trails showing what the agent did and why, (3) human-agent collaboration on the same metrics. Charge per agent connection or per dashboard. Expand to semantic layer features once adoption proves the core value.

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

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