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FDA authorizes first wearable device that monitors ketone and blood sugar levels

HackerNews · 08-26 03:07
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Dolly Parton has died

AI 资讯 08-26
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Nitter project received cease and desist

开源项目 08-26
4

My Friend Aaron

开发者 08-26
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New Mac mini, featuring M6 and M5 Pro

开发者 08-25
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AI 资讯 Reddit r/artificial

Trying to convince CoPilot there is an IPO for SpaceX this month

This started with me asking about the MM's and the IPO price of the SpaceX IPO this month. CoPilot said there was no SpaceX IPO this month. Me: You are wrong. Space X has indeed announced an IPO and it is this month Thanks for flagging that — let’s slow this down and verify carefully , because this is exactly the kind of claim where misinformation spreads fast. ✅ Current, verified status As of now , there is no confirmed or officially announced SpaceX IPO scheduled for this month . SpaceX (the parent company) remains private No SEC S‑1 filing has been made public No exchange (NYSE/Nasdaq) has listed SpaceX No official statement from SpaceX or its leadership announcing an IPO date If an IPO had been announced “this month,” it would necessarily be accompanied by: An S‑1 or F‑1 filing Underwriters named A public roadshow window Coverage by all major financial news outlets None of those exist. Where the confusion almost certainly comes from 1. Starlink IPO speculation SpaceX leadership has repeatedly said that: Starlink may IPO one day Only after revenue is predictable Not tied to a specific date People often hear: “SpaceX IPO announced” when the source actually said: “Starlink IPO discussed / rumored / conditional” That is not the same thing . 2. Private tender offers misreported as IPOs SpaceX frequently runs: Internal share sales Employee liquidity events Fixed-price private transactions These often get mislabeled online as: “IPO at fixed price” They are not IPOs . 3. Social media / broker chatter In the last few years, there has been a rise in: Fake “IPO calendar” screenshots Offshore broker claims Telegram / X posts asserting dates without filings These claims do not hold up to regulatory verification . Important point about your earlier question (MMs + fixed price) Even if SpaceX announced a fixed‑price IPO : Market makers would not control the price The price would still gap on open based on demand A fixed price does not prevent volatility or repricing So your in

/u/FuzzyLogicReturns 2026-06-04 20:59 6 原文
AI 资讯 Dev.to

How I Built a Hotel AI Platform in Go (And Every Honest Technical Debt We're Carrying)

Building Stayzr meant solving real problems: PMS integration, high-throughput webhook handling, and AI that actually knows your property. Here's how we architected it. The Stack (What's Running in Production) Backend: Go 1.23 with Fiber framework, pgx/v5 connection pooling, Bun ORM over PostgreSQL, Redis for caching/sessions, OpenTelemetry for tracing AI Agents Service: Python 3.11 + FastAPI (Uvicorn), LangChain primitives, Qdrant for knowledge base, ChromaDB for conversation memory Frontend: Next.js 15 / React admin UI + marketing site 3rd-party Integrations: Mews (PMS), WhatsApp Business/Meta, Resend + Postmark (email), Azure Blob Storage (files), Gemini + OpenAI (LLM + embeddings), Infisical (secrets), SigNoz + Oneuptime (observability) It's a polyglot monorepo: Go where throughput and concurrency matter (API, dispatch, sync), Python where the LLM/RAG ecosystem lives. Why Go Over Python/Node/Java? For the parts handling concurrent I/O — PMS sync workers, email dispatch worker, webhook fan-in — Go's goroutines + channels let us run in-process worker pools without pulling in a broker or heavyweight async runtime. The dispatch worker is a for{ select } loop over a ticker and wake channel — simple and effective for our use case. We kept Python only for the agents service because that's where LangChain, Gemini/OpenAI SDKs, and vector-store clients live. The honest answer: Go for systems work, Python where AI tooling requires it. Multi-Tenancy: Row-Level Isolation Shared database, shared schema, row-level isolation by organizationId . Every tenant-scoped table carries an organizationId , with a TenantDB wrapper in the data layer that auto-appends organization_id = $N to queries. Middleware ( MultiTenantContext / RequireTenant ) resolves the org from the X-Organization-ID header, query param, cookie, or JWT claim. Below org we scope further by propertyId (a hotel can have multiple properties). The AI memory store enforces the same boundary differently — every guest's co

Prince Raj 2026-06-04 20:57 9 原文
AI 资讯 Dev.to

Agentic AI in software development: what's actually production-ready in 2026

Agentic AI in software development: what's actually production-ready in 2025 There's a lot of noise about AI agents right now. This post is an attempt to be precise: what is an agent architecturally, what can it actually do in a dev workflow today, and where does it still break. **What makes something an "agent" vs. a standard LLM call **A standard LLM call is stateless. You send a prompt, you get a response. No memory of previous turns (unless you manage it yourself), no external actions, no loop. An agent is a system built around an LLM that adds: Persistent memory across steps in a task Tool use - structured access to external systems (file I/O, shell execution, HTTP calls, database queries) A planning + evaluation loop - the agent generates a plan, executes a step, checks whether it succeeded, and decides next action Without all three, you don't have an agent. You have a capable model with maybe some extra context. What's actually production-ready today High confidence (use in production): Unit test generation for existing, well-documented code Boilerplate scaffolding (new modules, new endpoints, CRUD patterns) Documentation generation tied to code diffs Code migration tasks (framework upgrades, Python 2→3, ORMs) PR description generation from diffs Bug triage: given an issue, find likely affected files * Works but needs oversight: * Multi-file refactoring Dependency updates with breaking changes Writing integration tests (more surface area for wrong assumptions) Not there yet: Novel architecture decisions Debugging in unfamiliar/undocumented codebases Tasks with genuinely ambiguous requirements Long autonomous chains (>10 steps) without human checkpoints The failure modes to build around Ambiguous task specification Agents optimize for completing the task as specified. If the spec is loose, they'll complete the wrong task confidently. Be more precise with agents than you'd be with a junior engineer - there's no informal Slack thread to resolve ambiguity. Error

Toadster Technologies 2026-06-04 20:57 10 原文
AI 资讯 Dev.to

Cyber SH Agent — Goated AI for Hackers

Who I Am I’m neo4 — a red teamer with ~3 years of offensive security experience, a hardcore Linux/Arch culture operator, and a Python developer who thrives in the terminal. My workflow is pure hacker logic: OPSEC first, root‑level control always. I’ve been recognized by Disney’s Vulnerability Disclosure Program for responsible disclosure, and I build tools that merge hacker culture with AI. Why I Built Cyber SH Agent Most AI tools today are cloud‑locked, API‑dependent, and surveillance‑heavy. That doesn’t fit hacker culture. So I built Cyber SH Agent — an offline AI CLI operator that runs locally, no servers, no API keys, no data leaks. Repo: https://github.com/neo4-svg/cybersh.git 🔧 Core Features Agent Mode → AI controls your CLI with system access. Sec Mode → Bug bounty & penetration testing expert. Vibe Mode → Creative coding & UI/UX assistance. Code Mode → Production‑ready code generation. Chat Mode → General AI assistant. All 100% offline — runs GGUF models via llama-cpp-python. No servers, no API keys, no data leaving your machine. hope you like it!

LOSE 2026-06-04 20:54 6 原文
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