DBDelve – Open-Source DB Client Made with Rust and GPUI
Hi HN, I’m Shayan and a few months ago I started building a DB Client DBDelve with a focus on performance and just making something that feels good to use (in the spirit of Zed, Ghostty, and other tools that just feel pleasant to use).For my workflow I’ve always looked for performance, especially since some time ago I had a really outdated machine and any kind of bloat was really noticeable, I remember using DBeaver and it just crashing on me multiple times, I also had to use postGIS which just made it worse. I migrated all my essential dev tools to use the ones with performance in mind (Zed, Ghostty etc) but the only thing that was missing was DB Client. The main choices everyone has are DBeaver which is a memory hog and an overkill for most of the tasks imo. The other options like Datagrid, Beekeeper, TablePlus are either electron based so bloated and memory hogs or paid and closed-source.DBDelve 0.3.6 against DBeaver 25.3.1 on the same Postgres table, median of 5 runs each (2026-10-11, M1 Pro, 32 GB). Times are from the action to the rows rendered. DBDelve DBeaver Launch 1.1 s 5.2 s Idle memory 133 MB 606 MB 1,000-row query 0.21 s 1.96 s 1M rows, time 56 s 208 s 1M rows, memory [1] 4.9 GB 17 GB [1] The whole 1M-row, 100-column table pulled into memory on purpose, with the row limit off. Not a normal use case; DBeaver's heap limit had to be raised manually for the benchmark.Scrolling perf with 1 million rows loaded with 100 columns each: https://youtu.be/V2MOhJYFqIEIt has most of the features most devs would need for 90% of their workflow plus some more I thought would be a nice touch: Readonly, read-write and Full Access modes Glass themes (If you’re into them, I am) Keyboard focused with every keybinding customizable Organization of connections into projectsMe and some other folks have been daily driving it for more than a mo
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
DBDelve is a fast, lightweight, open-source database client built in Rust targeting developers frustrated by bloated, slow tools.
The pain
The gap
Build angle
Strengths
- Clear measurable performance improvements over popular alternatives
- Open-source with potential community adoption
- Focused on developer experience and customization
- Founder has validated daily use and benchmarks
Risks
- Monetization strategy is unclear and user willingness to pay is uncertain
- Competition from established paid and free tools with larger ecosystems
- Niche market size may limit growth
- Sustaining development and community engagement long-term
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 90
- Solution gap
- 85
- Willingness to pay
- 70
- Buildability
- 85
The pain of slow, bloated DB clients is clear, there is a gap for a performant open-source alternative, some users would pay for better tools, and the founder can build it effectively.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The idea addresses a clear pain with measurable improvements, but monetization and market size are uncertain, limiting the overall value score.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The product fits a niche of developers needing speed and low resource use, with a credible technical approach and early user validation.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The solution is technically strong and addresses a real problem, but the competitive landscape and willingness to pay remain moderate concerns.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The founder has identified a real pain point and built a working product with clear performance advantages, though scaling 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!
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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