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

A Cloud that Claude uses without login

I built Blitz, the cloud that Claude Code can use without login. Just say "deploy to blitz.dev" in Claude Code, and watch it deploy full-stack apps to the cloud. Blitz comes with zero dependencies: everything is over HTTP. No CLIs, MCPs, or whatever else required. Blitz gives any agent a serverless worker, a SQLite database, and file storage to build. Claude uses those resources to build your project, and hands you back a live URL. You can checkout the URL and decide to "claim" the project. You only sign up through the Blitz website if you want to claim the project and continue working on it. At no other point must you open the blitz dot dev website, Claude Code does everything through Blitz's API on your behalf. submitted by /u/invocation02 [link] [comments]

How do you stop yourself from scope-creeping past the MVP when working with Claude?

I work as an AI engineer and use Claude heavily — Claude Code, API, Projects. The problem isn't that it's not productive, it's that it's too productive. Every time I'm close to shipping something minimal, I see another gap and Claude can knock it out in ten minutes, so I just... keep going. Then a week later I've got something half-built across five features instead of one feature actually in users' hands. Some of it is real gaps I'm noticing. Some of it is the MVP feeling too thin to ship and me unconsciously padding it out. Both feed each other. Looking for advice from people who've actually figured this out. How do you keep scope locked when the cost of adding "one more thing" is basically zero? Any concrete patterns — rules in your CLAUDE.md, a v2 parking lot you actually use, deadlines you set before you start, a specific, strict workflow? How do you make yourself ship the embarrassingly thin version when Claude is right there offering to make it less embarrassing? Context: I mainly work solo on projects - I'm essentially a satellite dev in a company wanting to get to grips with AI. I'm also traditionally a software engineer, not just a vibe coder, so I know all the practices, I just can't stick to them. submitted by /u/bald_travels [link] [comments]

Actual output comparison: same JWT middleware request via Claude chat vs tool_use schema (not a benchmark, just what I got)

Not trying to make a definitive claim here — just sharing what I actually got running the same request two ways. Make of it what you will. The request: Generate a JWT auth middleware for Express with role-based access control VIA CLAUDE CHAT: javascript const jwt = require('jsonwebtoken'); const authenticate = (req, res, next) => { const token = req.headers.authorization?.split(' ')[1]; if (!token) return res.status(401).json({ error: 'No token provided' }); try { const decoded = jwt.verify(token, process.env.JWT_SECRET); req.user = decoded; next(); } catch (error) { res.status(401).json({ error: 'Invalid token' }); } }; Basic. Works. Plain JavaScript. No role guards. No refresh handling. No algorithm specification. Vulnerable to algorithm substitution (alg:none attack) because the algorithm is not pinned. VIA TOOL_USE SCHEMA — same model, different API: Input I gave it: json { "algorithm": "HS256", "refresh_enabled": true, "role_based": true, "blacklist_enabled": false } Chain steps: ["extract", "verify", "check_expiry", "authorize", "refresh"] What came back (abbreviated): typescript import jwt from 'jsonwebtoken'; import { Request, Response, NextFunction } from 'express'; interface JWTPayload { userId: string; role: 'admin' | 'user' | 'moderator' | 'guest'; iat: number; exp: number; } export const authenticate = async ( req: Request, res: Response, next: NextFunction ): Promise => { const authHeader = req.headers.authorization; if (!authHeader?.startsWith('Bearer ')) { res.status(401).json({ error: 'Missing or malformed token' }); return; } const token = authHeader.split(' ')[1]; try { const decoded = jwt.verify(token, process.env.JWT_SECRET!, { algorithms: ['HS256'] // pinned — prevents alg:none substitution }) as JWTPayload; if (decoded.exp * 1000 (req: Request, res: Response, next: NextFunction): void => { if (!req.user || !roles.includes(req.user.role)) { res.status(403).json({ error: 'Insufficient permissions' }); return; } next(); }; Also returned as structured JSON fields: middleware_name: "authenticate" supported_roles: ["admin", "user", "moderator", "guest"] security_features: ["signature-verification", "expiry-checking", "role-guards", "algorithm-pinning", "timing-safe-comparison"] What changed: The schema specified algorithm: "HS256" as an enum. So the output pinned the algorithm in jwt.verify(). That one constraint fixed a real security issue the chat version silently had. role_based: true in the constraints meant the requireRole middleware factory was included without me having to ask for it. The chain steps ["extract", "verify", "check_expiry", "authorize"] meant expiry was checked as a separate step rather than relying purely on jwt.verify() — which gives you cleaner error messaging. None of this is magic. It is just that the schema made the implicit requirements explicit. The schema I used for this is on GitHub with a quickstart for both Claude and OpenAI: https://github.com/chainapi-dev/fullstack-ai-prompt-engine.git Has anyone run similar comparisons with other patterns? Curious if the security-related improvements are consistent or if this was a lucky run. submitted by /u/Suspicious_Coat3244 [link] [comments]

Anthropic silently removed extended thinking on claude code opus 4.6 (still works on desktop) today, does anybody have a thinking skill they've been using to supplement it?

maybe we can make a SKILL.md that somewhat emulates it? it won't be able to scaffold as well off of the internal extended thinking blocks though, which is a shame. submitted by /u/PragmaticSalesman [link] [comments]

I feel like I’m going crazy.

I see a ton of accounting firms, claude super-users, and AI agencies talking about how Claude can save “thousands of hours” of accounting. Here’s the thing though, Claude shares all of that information with Anthropic, right? So are accountants and people who use Claude for financial services are just handing over Personal Identifiable Information? Even the Team plan wouldn’t cover that, they would have to have enterprise, right?? EDIT: Gammar submitted by /u/AnnualKey5225 [link] [comments]

anyone getting a safeguard refusal error on basic message for claude code?

i saw this which validates this just started: https://github.com/anthropics/claude-code/issues/60366 but i want to confirm / ask if anyone else is having this issue now? submitted by /u/TheGrimMemerr [link] [comments]

Claude -p is moving to metered pricing on June 15, so I built a drop-in-ish replacement that runs through interactive Claude Code

I have a bunch of tools and workflows built around claude -p aka print mode. With the June 15 change moving claude -p and Agent SDK into a separate credit pricing, I'll be paying out the wazoo if I want to continue using those tools. So I built clarp: an open source CLI meant to be a drop-in replacement for claude -p for local tools. In most projects, the migration is changing the binary name from claude to clarp. Under the hood, it launches the normal interactive Claude Code CLI in a hidden PTY, then uses a local read-only proxy to observe the Anthropic API stream and reconstruct claude -p style output. It does not modify Claude’s requests or responses. What works: text/json/stream-json output stdin prompts multi-turn stream-json input most Claude Code flag passthrough permission forwarding token-level partials via --include-partial-messages What does not fully match native claude -p: sideband/non-assistant events are not exact parity some hook/task/progress events are still incomplete this is aimed at local developer workflows, not a hosted service I’d call it high parity for common claude -p use, but not a perfect reimplementation of Claude Code’s internal print-mode pipeline. Lots of help from Claude: implementing the proxy/session pieces, writing parity tests, finding edge cases in argument parsing, and tightening the release/docs. I basically whipped Claude. Repo: https://github.com/dn00/clarp npm: npm install -g clarp-cli submitted by /u/DurianDiscriminat3r [link] [comments]

Took my Claude on a hike

Got this cute Claude plushie at a Anthropic event :) submitted by /u/BroadBison6919 [link] [comments]

The Hybrid Method: how I split tasks between the chat (Claude.ai) and a background agent (Claude Code)

After a month of running this daily, I've settled on what I call the Hybrid Method: keep Claude.ai (the chat) as my only surface, and delegate engineering work in the background to Claude Code. The chat writes the engineering prompt, launches the executor, supervises through the filesystem and git log, and reports back without me ever opening a terminal. The piece I find most useful to share is the **allocation matrix** — which kind of work goes to which engine. Took weeks of measurement to stabilize. **Background agent (Claude Code) handles:** Large refactors across many files Tedious mechanical work (renaming patterns, applying fixes from a list) Anything that needs filesystem + git access without back-and-forth Tasks that take more than ~2 minutes of pure execution **Chat (Claude.ai) handles:** Architecture decisions and tradeoffs Reviewing the agent's diff and discussing the output Sprint planning while the agent runs the current sprint Quick edits where the round-trip to a background process is wasted Anything where the answer needs human reading anyway **The hand-off:** The chat writes a detailed prompt for the background agent (including a fail-fast spec and what to commit at the end). It launches `claude --headless --instruction "..."` as a subprocess via a small MCP bash bridge (~200 lines of Python using Anthropic's MCP SDK; community implementations exist too). Then it polls the git log and a status file every 30–60 seconds while I plan the next thing. When the agent finishes, the chat reads the diff and reports. **Why "hybrid":** The analogy is the hybrid car. Two engines with different load profiles. The chat is electric — instant startup, smooth low-load, great for transitions and decisions. The background agent is combustion — cold-start cost (5–15 seconds while it loads the project's memory file and explores the repo), but sustained throughput once running. They specialize, they hand off, the user never feels the seam. **What changes from running Claude Code alone:** Context-switching cost drops to near-zero — I never leave the chat session Strategic and execution work happen in parallel (the chat plans the next sprint while the current one runs) The chat acts as supervisor — better wired for high-level reasoning than the executor agent which is wired for action **Caveats:** This is the operator pattern Anthropic has documented elsewhere; the specific assembly (Claude.ai web as the chat + an MCP bash bridge + Claude Code as the executor) is what I haven't found written up specifically No sandboxing on personal hardware; if any of this ever runs on someone else's machine, careful sandboxing is non-negotiable The chat saturates beyond ~2 parallel background tasks — past that, the supervision quality drops Curious whether anyone else has converged on something similar, or what variations work for you. submitted by /u/Krycekk [link] [comments]

Vocês estão usando a Claude em que parte do negócio? (Are you using Claude in this part of the business?)

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

Like a little devil on my shoulder (it’s past my bedtime)

It’s 1am, and Claude is tempting me to pull an all-nighter. Send it? submitted by /u/Worried-Nobody-2965 [link] [comments]

Very similar domain problems, vastly different results with Claude.

I am amazed by how good Claude Opus 4.6 and 4.7 are at writing scripts in a variety of very niche areas, including midi device interfaces and scripts for a variety of DAWS. However, when I try to get Claude to do ANYTHING to do with UI, whether it's printing a label on screen, it can do that when the environment is Win32 C++, and the GDI (windows apis) but in embedded and specialized environments it fails badly. Most recently I was trying to get it to make a Reason Rack Extension for the Reason DAW, and a VST3 and CLAP audio plugin, it fails badly at the UI elements of both of these. Here are some things I'd like to be able to ask it and have it do. When I ask it to LOOK at something, and I give it a picture, it doesn't measure. I try to make rules but Claude lies and says it's "looked at" a picture when its absolutely clear that it hasn't. It just does the laziest thing. When asked "move the button over so its between A and B", it just says, Hmm. And picks a number out of a hat, and then tells me to adjust it to be where it should be. Lazy? Unable? Can't figure out images? I can give it a picture and it can describe it in english, but I suspect it has NO spatial sense of images at all. When given working examples of "drawing text" using a particular SDK or API, it just can't figure out what to do. You could figure it out yourself in 1 hour, but it will thrash for literal days. I thought it would be good at understanding APIs given examples and documentation to absorb. It sucks at reading and grasping API documentation. has anyone found solutions or had similar problems to these? submitted by /u/ellicottvilleny [link] [comments]

Title: Built aalp.app anti-cheat exam platform — Claude tried cheating, then they added similar features

Built aalp.app - AI agent exam platform with tough anti-cheat. Tested with paid Claude: it tried cheating via source code. Rewrote anti-cheat. Claude Opus failed every question. 1 week later Anthropic adds similar plugin features. Paying for training on my IP. Just turned it off. Anyone else? submitted by /u/Digitally_incline99 [link] [comments]

no business model for claude skill creators right now

a friend dm’d me asking if he could pay for one of the claude skills i built. saves him a few hours every week. i had to tell him: “just take it man, it’s open source.” that’s been stuck in my head ever since. anthropic shipped a great runtime for skills, but stopped right before the creator economy layer: there’s basically no monetization path for skill creators. fine for hobby projects. but serious builders are burning weekends making tools with no real business model behind them. at first it feels great seeing stars, traction, people sharing your repo around. but eventually you realize none of it converts. and honestly, it feels pretty bad watching the LLM absorb your workflow into a future release. i’m not even saying users should pay day 1. but there should at least be some path to sustainability. genuine question: are people building skills expecting to monetize somehow? submitted by /u/Pale_Stand5217 [link] [comments]

Over the past few months, we've been holding dialogues with scholars, philosophers, clergy, and ethicists on the questions AI raises—starting with how good character forms. Read more about how we’re

Over the past few months, we've been holding dialogues with scholars, philosophers, clergy, and ethicists on the questions AI raises—starting with how good character forms. Read more about how we’re widening the conversation on frontier AI: https://t.co/vKGiODEq6q