Turbolite – a SQLite VFS serving sub-250ms cold JOIN queries from S3
I built a SQLite VFS in Rust that serves cold queries directly from S3 with sub-second performance, and often much faster.It’s called turbolite. It is experimental, buggy, and may corrupt data. I would not trust it with anything important yet.I wanted to explore whether object storage has gotten fast enough to support embedded databases over cloud storage. Filesystems reward tiny random reads and in-place mutation. S3 rewards fewer requests, bigger transfers, immutable objects, and aggressively parallel operations where bandwidth is often the real constraint. This was explicitly inspired by turbopuffer’s ground-up S3-native design. https://turbopuffer.com/blog/turbopufferThe use case I had in mind is lots of mostly-cold SQLite databases (database-per-tenant, database-per-session, or database-per-user architectures) where keeping a separate attached volume for inactive database feels wasteful. turbolite assumes a single write source and is aimed much more at “many databases with bursty cold reads” than “one hot database.”Instead of doing naive page-at-a-time reads from a raw SQLite file, turbolite introspects SQLite B-trees, stores related pages together in compressed page groups, and keeps a manifest that is the source of truth for where every page lives. Cache misses use seekable zstd frames and S3 range GETs for search queries, so fetching one needed page does not require downloading an entire object.At query time, turbolite can also pass storage operations from the query plan down to the VFS to frontrun downloads for indexes and large scans in the order they will be accessed.You can tune how aggressively turbolite prefetches. For point queries and small joins, it can stay conservative and avoid prefetching whole tables. For scans, it can get much more aggressive.It also groups pages by page type in S3. Interior B-tree pages are bundled separately and loaded eagerly. Index pages prefetch aggressively. Data pages are stored by table. The goal is
FL score
out of 100
Verdict
low confidence
Competition
No competitor data yet
Trend
No signal yet
A SQLite VFS for S3 promises sub-second cold queries for multi-tenant architectures, significantly reducing costs and improving performance, but requires extensive validation and stabilization.
The pain
The gap
Build angle
Strengths
- Addresses a specific, high-value architectural pain point (cost and performance of cold SQLite data at scale).
- Technically innovative approach (S3-native VFS, B-tree introspection, prefetching) creates a strong differentiator.
- Clear, measurable value proposition: cost savings and improved query performance.
- Strong technical expertise demonstrated by the builder.
- Growing market for serverless/edge computing and multi-tenant architectures where this solution fits.
Risks
- The current state is 'experimental, buggy, and may corrupt data,' which is a critical showstopper for adoption.
- High complexity for a solo builder to stabilize, make production-ready, and ensure data integrity.
- Monetization and go-to-market are challenging until reliability is unequivocally proven.
- The niche, while specific, might be smaller than initial perceived market size, requiring precise targeting.
- Lack of current user validation or observations due to the project's early stage.
Next steps
- Prioritize stabilization and robustness: eliminate data corruption risks and ensure production-grade reliability.
- Develop a comprehensive test suite covering edge cases, concurrent access, and various query patterns.
- Create benchmarks demonstrating consistent sub-250ms cold query performance under realistic loads.
- Seek feedback from potential target users (architects, senior developers) on the stability and performance requirements.
- Focus on a minimal viable 'reliable' product that solves just the core pain point before adding more features.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 24
- Solution gap
- 22
- Willingness to pay
- 19
- Buildability
- 11
Addresses a real, specific architectural pain point in multi-tenant SQLite cold data management with a clear technical gap, but is highly complex to build and stabilize as a solo project.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
A technically innovative solution with a clear value proposition for a growing market, but held back by its current experimental state and high build complexity.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A complex, niche technical solution with strong leverage, but requires substantial effort to move from experimental to a simple, reliable product for a solo builder.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
A micro-SaaS with a clear target and value prop, but critical risks around stability and validation must be addressed before viability.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
A promising idea targeting a real architectural pain, but currently lacks product reliability and user validation to be funded by YC.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Prioritize stabilization and robustness: eliminate data corruption risks and ensure production-grade reliability.
- Develop a comprehensive test suite covering edge cases, concurrent access, and various query patterns.
- Create benchmarks demonstrating consistent sub-250ms cold query performance under realistic loads.
- Seek feedback from potential target users (architects, senior developers) on the stability and performance requirements.
- Focus on a minimal viable 'reliable' product that solves just the core pain point before adding more features.
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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