How do you interview devs in a post-AI world?
Since the rise of AI coding assistants, about 80% of the dev candidates that I interview tell me that they aren't writing much code themselves anymore - they are directing agents instead. This makes me deeply uncomfortable (although maybe I'm just being old-fashioned). I still want to know that devs on my team can actually write code themselves, and have some idea of what they are doing when it comes to system design. This is increasingly challenging given how rapidly the AI tools are evolving.I'm curious what other folks are seeing in a post-AI hiring landscape and how you are approaching candidates that tell you they are fully agent-pilled when it comes to their development process. Should I keep doing the conventional leetcode and design interviews? Or just give up and expect everyone is going to use Claude Code no matter what?
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
A hiring manager is uncomfortable that AI-assisted developers no longer write code by hand, but no one has figured out how to assess real coding ability in a post-AI world.
The pain
The gap
Build angle
Strengths
- The problem is growing and will only get worse as AI tools improve
- Hiring managers are actively struggling with this right now, not in theory
- The pain is concentrated in tech companies with real hiring budgets
- Early movers could establish credibility in a new category
Risks
- No consensus on what good assessment looks like in an AI world, so product-market fit is unclear
- Hiring managers may just change their interview process themselves rather than buy a tool
- Candidates will find ways to use AI during any assessment, making enforcement hard
- Existing assessment platforms have distribution, brand, and customer relationships
- The market is small, limited to tech hiring at companies that care about this specific issue
- The problem may self-correct as companies accept that AI-assisted development is the new normal
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 75
- Solution gap
- 35
- Willingness to pay
- 40
- Buildability
- 25
The problem is real and growing, but no clear solution exists yet and it is unclear if hiring managers would pay for a tool versus just changing their interview approach.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The pain point affects a specific audience, but the business model is murky, the market size is limited to tech hiring, and there is no obvious way to charge enough to build a sustainable company.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Hiring managers feel the discomfort acutely, but the solution space is fragmented across assessment methods, interview formats, and company culture, making it hard to build one product that solves the core problem.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The problem is emerging and real, but the person posting is asking questions rather than proposing a solution, and the market for hiring tools is crowded with entrenched players like HackerRank and Codility.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
This is a niche problem within a niche market, lacks a clear go-to-market strategy, and does not have the scale or defensibility that YC typically funds.
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
GlycemicGPT – Open-source AI-powered diabetes management
I'm a Type 1 diabetic and software engineer. Last year I went months between endocrinologists with no clinician reviewing my data. I'm an engineer, so I built the tool I needed — and now I'm open sourcing it. GlycemicGPT is a self-hosted platform that connects continuous glucose monitors, insulin pumps, and existing Nightscout instances to an AI analysis layer running on your own infrastructure. Data sources:Dexcom G7 (cloud API) Tandem t:slim X2 and Mobi pumps (direct BLE) Nightscout (point it at your existing instance and you're running in minutes)What the AI layer does:Daily briefs summarizing overnight and 24-hour patterns Meal response analysis Conversational chat with RAG-backed clinical knowledge Predictive alerting with configurable thresholds and caregiver escalationImportant: this is monitoring and analysis only. GlycemicGPT does not deliver insulin, does not control your pump, and is not a closed-loop system. It reads your data and gives you insight on top of it. Your clinical decisions stay between you and your care team. Architecture:Self-hosted via Docker or K8S — the GlycemicGPT stack runs entirely on your hardware BYOAI — bring your own AI provider. Use Ollama for fully local operation (no data leaves your hardware), or point it at Claude, OpenAI, or any OpenAI-compatible endpoint if you prefer a hosted model. Data flows directly from your instance to the provider you choose; nothing is routed through any centralized service operated by the project. GPL-3.0, no subscriptions, no vendor lock-inStack:Backend API: FastAPI, Python 3.12, PostgreSQL 16, Redis 7 Web Dashboard: Next.js 15, React 19, Tailwind CSS, shadcn/ui AI Sidecar: TypeScript, Express, multi-provider proxy Android App: Kotlin, Jetpack Compose, BLE Wear OS: Kotlin, Wear Compose, Watch Face Push API Plugin SDK: Kotlin interfaces, capability-based, sandboxedLooking for contributors — especially folks with BLE/Android experience or anyone in the diabetes tech spa
AI
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