Anthropic Claude API

Anthropic Claude API AI

Anthropic Claude API AI

Anthropic Claude API AI

Anthropic Claude API AI

Anthropic Claude API AI

Anthropic Claude API AI

Anthropic Claude API AI

Anthropic Claude API AI

Anthropic Claude API AI

Anthropic Claude API AI

Best stack for a personal AI voice agent that calls me and walks me through packing?

I’m an engineer trying to build a small personal tool for myself, not a business/customer-service bot. Use case: I have ADHD and travel a lot. I want an AI voice agent that can call me before a trip and walk me through a packing list that I previously entered by text or voice. The important part is that it needs to be interactive. I don’t just want it to read a checklist out loud. I want something like: AI: “Do you have your passport?” Me: “Wait, let me go get it.” AI: “Okay, I’ll pause.” Me: “Got it, continue.” AI: “Next: chargers.” Requirements: Outbound call to my phone based on criteria/schedule/trip timing Natural voice conversation Ability to pause/resume Ability to track checklist state Ability to mark items as packed, skipped, or still needed Ideally cloud-hosted, but I could run something locally on an old laptop if that is genuinely better Could use Twilio, Vapi, Retell, OpenAI Realtime API, Claude/Anthropic, local LLMs, or another stack What I’m trying to figure out: What is the simplest current architecture for this? Would you use a voice-agent platform like Vapi/Retell, or build directly with Twilio + OpenAI Realtime? Is Claude/Anthropic useful here, or is OpenAI better because of realtime audio? How should I store and update checklist state during the call? Is local hosting worth it for this, or is cloud clearly the better choice? Are there any gotchas with outbound calling, latency, interruptions, or conversational state? I’m optimizing for reliability and simplicity, not making a startup product. I just want something that actually helps me pack without needing to stare at my phone. submitted by /u/Maroontan [link] [comments]

AI pair programming has a process problem — here's what I built

TL;DR: I built ai-flow-anything — a markdown-native workflow generator that interviews your codebase, detects your project type, and produces design-first flows with a knowledge base that audits itself against your code. No build step, no CLI, no DSL — just markdown. Works with Claude Code, Cursor, GitHub Copilot, OpenCode, and Kimi Code. MIT licensed. The problem I've been wrestling with: AI coding assistants are incredible at writing code, but they're terrible at process. Every new task feels like starting from scratch — no design doc, no architecture context, no trace of why decisions were made. The AI codes before it thinks. And when you switch between Claude, Cursor, or Copilot, you lose all context. Another problem: when I move to a new project, I lose all the setup and skills I fine-tuned for the last one. What I wanted: Design-first, enforced — no task code until a design is signed off, and every phase ends at an explicit [A]ccept / [F]eedback / [R]eject gate. The AI never decides its own work is done. Works with any AI assistant (not locked to one tool) Auto-detects your project type and tailors workflows accordingly A knowledge base that stays true — not just docs that rot Tracks every task from design → implement → test → PR → deploy How it works: Clone into your project: git clone https://github.com/yusufkaraaslan/ai-flow-anything.git .ai-workflow Initialize — your AI detects the project type (Unity, Godot, React, FastAPI, Flutter, …), interviews the codebase, and asks about your goals Get 9 tailored flows — design, implement, free (quick fixes), parallel-implement, PR, test, deploy, docs, and KB sync The repo gives you two directories: .ai-workflow/** (the engine — instructions, universal rules, stack-specific profiles, and 9 rendered flow files) and **flow-storage/ (the knowledge base — project architecture, team docs, and per-task records with immutable design docs, edge cases, diagrams, plans, and lessons learned). How I developed it: I use AI to develop my games. While I work, I look for patterns that keep repeating and I note them down. After collecting enough notes — say, across two or three tasks — I create a basic skill with Claude to automate part of the work I'm doing. Over time, those skills become fine-tuned and tailor-made to individual projects and different types of tasks. Then I move to a new game, extract the soul of the workflow, and repeat the process. Eventually, through trial and error, ai-flow-anything formed itself. Battle-tested, including the failure: I dogfooded an earlier version for four months on a Godot and unity games I'm building. The per-task side worked genuinely well — 16 features shipped through full design packages with rendered PlantUML diagrams, and the implementation plans drove real commits. Two favorite bits: the AI implements independent work packets in parallel via git worktrees and merges them in dependency order, and designs are immutable after sign-off (deviations get recorded separately, so the record stays honest). But the cross-task knowledge base quietly went stale — months in, it contradicted the live codebase (wrong test framework, a "hard" constraint that had been relaxed). A stale KB is worse than an empty one, because the AI loads it as trusted context on every run. v1.0.0 exists because of that failure: every flow now spot-checks KB claims against reality before trusting them, a status command audits for drift, and a KB-sync flow walks every claim (claim → observed reality → proposed fix) and repairs the records at review gates. The philosophy: AI is the engine — all instructions are prose markdown the AI reads and follows Documentation-first — design before a single line of code Trust requires verification — docs that can drift must be checked against the code Customizable by editing markdown — add rules, phases, or entirely new flows Supported stacks: Unity, Godot, React/Vue/Angular/Svelte, FastAPI/Django/Express/Go, Flutter/React Native, plus a generic fallback. Platforms: Claude Code, Cursor, GitHub Copilot, OpenCode, Kimi Code CLI — thin wrappers, same workflow logic. My question to you: How are you keeping your AI's knowledge base from going stale? I'd love to hear what's working and what's broken. submitted by /u/Critical-Pea-8782 [link] [comments]

A system-level approach to prompt injection: separating instruction and data channels in LLM agents [P]

Prompt injection has emerged as one of the most persistent failure modes in tool-using LLM systems, particularly in agentic workflows where models interact with external data sources. Most mitigation strategies focus on input filtering or model-side alignment, but these approaches struggle because the core issue is structural: Approach I explored a system-level mitigation strategy by introducing a middleware layer (Sentinel Gateway) that enforces a strict separation between: Instruction channel: trusted, runtime-issued commands Data channel: untrusted external inputs (web, files, APIs) Instead of attempting to classify malicious inputs, the system ensures that: All agent actions require a signed, scoped runtime authorization token, effectively decoupling observation from execution. Implementation FastAPI middleware layer for agent tool calls Token-based authorization for execution requests Streamlit interface for inspection and debugging Audit logging of agent decisions and tool usage Supports multi-agent integration patterns (e.g., Claude-based sessions) Local or Postgres-backed persistence layer Repo https://github.com/cmtopbas/Sentinel-Gateway Discussion question I’m interested in feedback on: whether instruction/data separation is a meaningful abstraction for agent safety failure modes in token-based execution gating how this compares conceptually to other agent safety or sandboxing approaches submitted by /u/vagobond45 [link] [comments]

Fable 5 releasing tomorrow: does it mean GPT-5.6 Sol is coming too, or is it only starting its 3 weeks government review

submitted by /u/py-net [link] [comments]

TFW

submitted by /u/EchoOfOppenheimer [link] [comments]

GPT-5.6 Sol is out — but only ~20 government-approved companies can use it

OpenAI announced GPT-5.6 Sol on June 26. Three tiers: Sol (flagship), Terra (mid), Luna (cheapest). Sol scores 88.8% on Terminal-Bench 2.1 — slightly ahead of Claude Mythos at 88.0%. Ultra mode hits 91.9%. Pricing: Sol $5/$30, Terra $2.50/$15, Luna $1/$6 per 1M tokens. The catch: ~20 companies got access, each individually approved by the US government (White House ONCD + OSTP). No public waitlist. No self-service enrollment. Same restriction they put on Anthropic's Fable 5 three weeks ago. OpenAI says broader availability "in coming weeks." More info here: Youtube Sources: - openai.com/index/previewing-gpt-5-6-sol/ - techcrunch.com/2026/06/26/openai-limits-gpt-5-6-rollout-after-government-request/ What's your take — is government-gated access becoming the norm for frontier models? submitted by /u/Low_Edge7695 [link] [comments]

What happens when you give an AI a budget?

Over the past few weeks, I've been exploring whether LLMs can work with execution budgets. We've seen models produce meaningful artifacts with projects like Caveman and Ponytail, but what happens when you give models limited Budget. Researchers at arXiv:2606.00198 recently found that frontier models are consistently over-optimistic about budget. Instead of stopping and alerting the user, they keep spending tokens on work that's unlikely to succeed. Which made me try something simple I started giving Claude implementation tasks with a fixed execution budget. The behavior changed. Instead of trying to build everything, it focused on completing the requested work before asking for more budget. The unconstrained version as compared to the budget-constrained version had a lot of stuff, i didnt need for my immediate work Across three implementation tasks (two REST APIs and a Python CLI), output dropped by 46–60% while still completing every requested task. One Bookmark Manager task finished in about 1,600 tokens, while the unconstrained version was still generating when i pulled the plug I built a small runtime called Token Sensei that enforces these execution budgets. When the budget runs out, it pauses, shows what's complete, what remains, and lets the human decide whether to continue or ship the current result. It's open source (MIT): github.com/shouvik12/token-sensei Please let me know your thoughts on this and if this helps you submitted by /u/Substantial_Load_690 [link] [comments]

Consumer Ai Models is Dumped?!

IMPORTANT: Friends, please share your own experience in the comments. I do not believe I am the only one who has noticed this. My honest impression ⚠️ I am not claiming I know exactly what is happening internally. But after regulators in the US started putting more pressure on companies like Anthropic and OpenAI, it really feels like public-facing models may be getting increasingly: restricted; over-filtered; behaviorally “flattened”; less decisive; less capable of following a long technical chain without drifting. Maybe it is safety tuning. Maybe cost optimization. Maybe routing. Maybe model switching behind the scenes. Maybe a mix of all of it. But the result for power users feels the same: You can no longer treat API models as a stable foundation without verification. It gave me an answer that sounded confident — but I already knew that one part of it was 100% wrong. So I pushed deeper: asked follow-up questions; asked it to verify the setup; asked it to do proper research; pointed out the contradiction. Eventually, it admitted that it had made a mistake. Fair enough. Models make mistakes. But then it happened again. And again. The local-model test 🖥 For context: I have 96 GB of VRAM across 4× RTX 3090s; the system was built specifically for multi-agent work; the point is to run several agents/models without everything choking on VRAM limits or waiting in a queue. So I asked Claude a pretty straightforward question: Recommend a local model that is genuinely better than the one I currently use. I will not name my current model because this is not meant to be an advertisement. Claude started suggesting models weighing around 200–300 GB. That made me stop immediately. My current model came out only a few months ago. Some of the alternatives it recommended were already around a year old. Bigger does not automatically mean smarter — especially when newer architectures, better training data, reasoning tuning, and MoE designs exist. So I asked for open benchmark comparisons. And once the numbers entered the room, the answer started changing. Again. 📉 Then it failed a very basic infrastructure task ⚙️ I gave it another simple scenario: I want GPU #4 to run a bot with one specific job. I want to replace the model currently on that GPU with a Qwen model chosen for that role. New session. Fresh context. Claude began calculating VRAM usage and then told me the LLM model would not fit because another model was already loaded on GPU #4. That was the whole point. I was talking about replacing the old model. Not running both. Not stacking them. Replacing. And after missing that basic detail, it started slipping even further: misunderstanding simple instructions; losing context inside a short conversation; failing elementary calculations; giving confident but internally inconsistent answers; correcting itself only after being pushed hard. Not some insane edge-case prompt. Not advanced research. Just basic logic. My personal conclusion 🔥 For me, it is not a disaster. I have two rigs with 10× RTX 3090s total, so I am not trapped inside one subscription or one company’s changing model behavior. But for anyone whose actual work depends on AI, I would seriously start thinking about this now: build workflows around local LLMs; train role-specific agents; keep your own benchmark set; save working model versions; do not rely entirely on one API provider; treat every confident answer as something that may still need verification. Because the situation is starting to look less like: “Which model is smartest?” And more like: “Which model will still behave consistently next month?” I have used GPT seriously for around a year, and Claude for roughly half a year. And honestly? I have never seen this kind of regression so clearly before. submitted by /u/3xQuest [link] [comments]

Anthropic speaks out

submitted by /u/ConsiderationHour710 [link] [comments]

Update: Higher rate limits on Claude API

submitted by /u/iamjohncarterofmars [link] [comments]

Trump admin allows Anthropic to release Mythos AI model to some companies, government agencies: Reports

Source: https://www.cnbc.com/2026/06/26/us-government-anthropic-claude-mythos5-ai.html?__source=iosappshare%7Ccom.atebits.Tweetie2.ShareExtension submitted by /u/thelastsubject123 [link] [comments]

Claude and dopamine

Hey, for people who use Claude Code heavily, don’t you feel like it gets kind of addictive? I have a $200 plan, another $100 plan, and my company gives me $1k to spend, which they’ll probably raise to $1.5k soon. And I know my personal plans are heavily subsidized, because they go way further than the $1,000 I get from the company to spend through the API. It’s like you just keep creating, creating, creating. submitted by /u/GordoTetoncito [link] [comments]

I built a free Claude connector that auto-syncs your conversation history into Obsidian (+ symlinks Claude's own memory so you can browse what it "knows")

the thing that frustrated me was losing context between claude sessions. i'd work through a real problem, figure something out, and a week later i'd be starting from scratch so i built obsidian-vault-sync. it reads claude code's local .jsonl transcripts and automatically converts them into organized markdown notes in obsidian a few things that make it different from the other sync connectors i've seen: no API calls at all. classification is done with weighted keyword matching (conversation titles weighted 3x vs opening prompts) so it's completely free to run, zero model API cost the memory symlink. this is the one i keep showing people. it symlinks claude's memory folder directly into your vault, so the notes claude keeps about your work become real obsidian notes you can browse, edit, and backlink. you can literally see what claude "knows" about each of your projects just shipped: vault-worthy filtering. based on feedback from r/ObsidianMD, i added a flag so you can mark specific sessions as vault-worthy before syncing. right now it pulls everything in by default, but a lot of people only want the sessions that actually contain something worth keeping, so this felt necessary one thing i want to be upfront about: it's currently one-way and static (claude → obsidian). the note doesn't live-update as a conversation continues, it captures a snapshot on sync. that's on the roadmap but not there yet works with cron/launchd to auto-sync on a schedule, python only, no paid dependencies github: https://github.com/arya51-ai/obsidian-vault-sync happy to answer questions, especially about how the classification works. i use this daily so i'll keep it maintained 🙌 submitted by /u/aryamehta [link] [comments]

shipped 5 brownfield apps with planted api bugs. test if claude code (or any mcp agent) catches them

built an MCP server (FetchSandbox) that ships a curated "brain" per third-party API, stripe, resend, clerk, twilio, etc. each brain encodes real bug patterns: symptoms → likely cause → reproduce workflow → fix pattern. wanted to stress-test it against bugs that actually bite people. so i made 5 small brownfield FastAPI apps, each with one planted bug: stripe dedup keyed on the wrong header so the same event fires 2-3x on retry, clerk JWT verification disabled so anyone can mint an admin token, surge SMS retry re-sends to the wrong number with the opt-out webhook silently dropped (TCPA risk), and two others. each app is ~50-150 lines of python, .mcp.json wired, clone and point your agent at the dispatch prompt: github.com/fetchsandbox/playground first 24 hours: one PR from a stranger fixed the surge bug and flagged two gaps in my brain content. one volunteer's session caught a bug in MY system, the agent surfaced a fake proof URL because the MCP wasn't propagating share_url. fixed same day. three things i'm genuinely stuck on and would love input from people actually running MCP in prod: brain-as-yaml (symptoms → fix_pattern in static yaml) vs lighter prompts that let the agent read code and figure it out. which curation level is actually useful vs just noise? how do you prove an agent's fix worked when your test infra can't reach the handler? my receipts right now prove input state (stripe replayed the event 3x with the same id) but not behavioral diff. building handler-inline observation next but curious if anyone's closed this cleanly. routing fallback when router confidence is below threshold. mine abstains today, which means the agent falls back to plain grep+read+edit and the MCP adds zero value to that session. leaning toward a cascade to a small LLM judge with ranked candidates, but interested in patterns people have shipped. findings go in findings/ as a PR. merged ones show up on your contribution graph. honest negative findings are the most useful, already got one, fixed it same day. submitted by /u/Common_Dream9420 [link] [comments]

echo•mux: a self-hosted multi-room Bluetooth audio streamer with per-speaker latency alignment and Spotify Connect support

Hey everyone! A couple of weeks ago, I was lying in bed, listening to Spotify, looking around at my mix of premium and budget Bluetooth speakers from different brands, and thought: “Why can’t I just stream to all of these simultaneously?” As a long-time developer, I thought I could for sure solve this somehow with the hardware I already own, and did my research. I found the industry goes towards Wi-Fi-based systems, Sonos and whatever, but I love my new Edifiers, my old Sony, and my Sackit (anyone remember that?), and I see no reason to buy something new. I realized the latency of different brands of speakers or locations is the real problem, and soon I had an architecture in mind. So over the last two weeks, I sat down in my spare time and built echomux together with Claude. I love it allowed me to follow test driven development principles, which usually are too much work. It turns my Raspberry Pis (I own two of them) into a multi-room Bluetooth hub controlled via a mobile-first web UI. For 10 bucks each, I bought additional Bluetooth antennas to get even better coverage to the backyard. I really just built it for myself to solve my own problem and have no plans to release a real product. Getting it to work reliably was hell, though. I ran into massive roadblocks, like a bug in the current repository version of BlueZ that makes muxing to multiple devices impossible, audio synchronization issues, and getting the UI right. But it’s finally working perfectly for my needs, so I figured I’d open-source it in case someone else is facing the exact same frustration. What it does: Spotify Connect: It exposes itself as a standard Spotify Connect device (powered by librespot). No custom music player app needed. Per-Speaker Latency Adjustment: You can adjust the delay (0–2000 ms) for each speaker individually through the UI to fix room alignment and BT buffering sync issues. Multi-Node / Satellite Support: Since a single Pi can't reach across a whole house via BT, you can deploy satellites on additional Pis. The master streams audio to them via RTP unicast, but you control everything from a single central UI. Tech Stack: Built in Go (single binary) for Linux with systemd, leveraging PipeWire, WirePlumber, and BlueZ. The UI is built with Svelte. If anyone wanted to build a native app, I included the agent instructions to do so. But the current web-based approach "just works" regardless of whether I am on the PC or on the phone, which I really like. It’s completely open-source now (Apache 2.0) and installs via an interactive setup script, which also builds BlueZ from source to fix the buggy repo version. I plan to maintain and develop this further as I come up with new ideas or find things that annoy me, aiming to make it even more stable and user-friendly. Check out the repo, architecture diagrams, and API specs here: https://github.com/dolphprefect/echomux It does the work for me now. I hope it’s useful to some of you too, enjoy. submitted by /u/Purple_Hornet_9725 [link] [comments]