Is it impossible to disable Siri on macOS 27?
I disabled Siri in the Settings app, and then further disabled Siri and its extensions in Settings > Screen Time.Next, I disabled all Siri-related and Apple Intelligence-related services as per https://github.com/OleksandrKrupko/mac-os-debloatBut after restarting, I could still see a "Siri" process running in Activity Monitor. Upon further inspection, I found that it is the "Siri AI.app".These "features", together with Apple's Personal Context sound very similar to Microsoft's Recall.Does anyone have the same experience? Is it really impossible to fully turn off Siri, Siri AI, Apple Intelligence and all related services and features on macOS 27?
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
A tool to fully disable Siri and Apple Intelligence on macOS solves a real but niche problem for privacy-conscious power users with no clear business model.
The pain
The gap
Build angle
Strengths
- Real pain point for privacy-conscious users who distrust Apple.
- Low technical barrier to build an initial version.
- Potential to expand into a broader macOS debloating tool.
- Growing awareness of privacy issues makes the audience more receptive than five years ago.
Risks
- Apple may patch the tool with each OS update, making maintenance a constant burden.
- Legal risk if Apple claims the tool violates terms of service or copyright.
- Tiny addressable market. Most users either accept Siri or do not care. Power users often use Linux instead.
- No recurring revenue model unless you build a subscription, which requires ongoing engineering effort.
- Reputational risk if the tool breaks user systems or fails to work as promised.
- Users expect this functionality to be free since it is just toggling settings.
- No defensibility. Once you publish the steps, anyone can fork or copy the solution.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 72
- Solution gap
- 48
- Willingness to pay
- 28
- Buildability
- 28
Users face a real problem disabling Siri on macOS, but the solution gap is unclear and most users lack willingness to pay for a workaround.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The market size is tiny, the pricing power is weak since users expect free OS control, and the distribution channel does not exist.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
This solves a niche technical problem for power users but lacks a clear monetizable outcome or recurring revenue model.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The problem is real but affects only a small percentage of macOS users, and any solution would require constant maintenance as Apple updates the OS.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
A tool to disable Siri addresses a real frustration but lacks defensibility, network effects, or a path to significant scale.
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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