Brave Search API

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Building in Public: Vibe Coding my Chrome Extension for Bloggers. PART 1

https://preview.redd.it/kdkh5v3fx43h1.png?width=640&format=png&auto=webp&s=75850b6e3fd69cda9a3c97e1190fcd506e11c2a6 For a while now, I have been learning Vibe Coding by creating plugins for WordPress , Chrome Extensions, and others. Thank God, all of them have been useful to me, but my inclination and passion has always been blogging, and Pinterest has been my companion for getting traffic. So I said why not make a more practical tool that would be useful to bloggers, so I made several copies over the past months, but perfectionism was preventing me from bringing the project to light, until I decided that this time would be the last, and in order to avoid perfectionism, I decided to build it in public. My first post on Reddit about my project has ended, and I will try to provide you with updates every two or three days. Currently, I have built about 90% of the extension, and not much remains to be launched, but I will add many features later. Perhaps some will ask: Have you made sure that the tool will be useful or needed? I can say yes because I am the first customer and user of the tool because it will actually save me time and effort and bring together everything I need as a blogger and Pinterest user in one place. Before I begin, I forgot to tell you that the tool is currently intended for bloggers in the cooking niche (my niche) and recipes, and in the upcoming updates, I will transform it to include all or most of the niches. Without further ado, these are the most important features of the Chrome extension: - Search tool: You can search for target words and know the monthly search volume on them. - Writing articles: You can write amazing articles individually or several articles together. You can create custom images for Pinterest. - Pinterest: You can create Pinterest-specific images for one or more articles and you can download them directly (title, description, images) - Amazon products: If you are a beginner or a new blogger, you can earn from the first day of blogging by adding Amazon products to market in exchange for a commission. Just search for the product, locate where it appears, and list it. - Inserting WordPress: Through it, you can link your blog directly to the extension, and from it you can publish articles on your blog without copying and pasting, and you will find within it even Amazon products that you added in the extension. The beautiful thing about the whole thing is that the tool has many details that I did not Mention, which is what makes it truly special. The most beautiful thing is that the extension works with your API and you can choose from 3 service providers, and this is what makes you the winner and you will only pay for what you will use and consume? Finally, I hope you will not be stingy with your advice and guidance Do you find that the tool is really useful or not? disclaimer: 99% of this post is translated because i am not english native, but its 0% Ai so please no one comment: Ai slop .... submitted by /u/motivational_speech1 [link] [comments]

I built an MCP server for osu! — Claude analyzes your stats in plain English (on the official MCP Registry)

Built osu-mcp — an MCP server that lets Claude Desktop (or any MCP client) talk to the osu! API v2. Just got it published on the official MCP Registry as io.github.Osyanne/osu-mcp. **Real demo I ran on my own account:** > "Show me my top 10 plays and then compare me with the top 5 players from Ecuador." Claude pulled my top plays (208.88 pp Dear My Friend DT, 206.33 pp happy*lucky DT, etc), fetched the EC country leaderboard, and computed pp-per-play efficiency across all 3 of us. Turned out my accuracy (98.18%) is identical to the #1 player in my country — what I'm missing is volume, not skill. Useful insight I'd never have computed manually. **What it does — 12 tools:** - Player profiles + score history (best / recent / #1s) - Beatmap search with filters (BPM, difficulty, length, status) - Global + country pp rankings - Per-map leaderboards, filterable by mods - News posts + seasonal backgrounds Install: uv tool install osu-mcp Create an OAuth app at https://osu.ppy.sh/home/account/edit (click "New OAuth Application", leave callback blank), then add to claude_desktop_config.json: "osu": { "command": "uvx", "args": ["osu-mcp"], "env": { "OSU_CLIENT_ID": "...", "OSU_CLIENT_SECRET": "..." } } Restart Claude → done. Repo: https://github.com/Osyanne/osu-mcp PyPI: https://pypi.org/project/osu-mcp/ MIT, PRs welcome. submitted by /u/Kingleyend [link] [comments]

Built a program to give my parents a 2nd look on suspicious emails/etc

My parents tech literacy is bad. They will have me check clear as day scam emails and the likes out way too damn often. To save my sanity, I finally used Claude Code to create a solution, hopefully.... Heck, even if it helps a bit, I will be happy. Not a 100% for sure thing, which I will stress to them when I show both how to use it. Used some APIs from virustotal and gemini for some of the features. Included some other resources for the different checks that search whatever entered along with taking you to said sites page of it searched. Any recommendations to improve this so it acts as a buffer between my parents and I? Definitely going to improve UI so it is easy to see(colors and text size) submitted by /u/LouB0O [link] [comments]

Built an MCP server for cross-Asia streaming queries. Claude can now answer "where can I watch X in Y" for 30 markets.

The problem: I run ottasia.com, a streaming aggregator for 30 markets across South Asia, SEA, East Asia, Central Asia, the Levant, the Gulf, and MENA. Users were asking Claude, "Where can I watch [show] in [country]" and Claude was just guessing, sometimes confidently wrong, sometimes citing JustWatch, which has thin coverage outside the US/UK. What I shipped today: an MCP server that gives Claude live access to OTTASIA's data. Install in the Claude Desktop config: { "mcpServers": { "ottasia": { "command": "npx", "args": ["-y", "@ottasia/mcp-server"] } } } Restart Claude. Three tools become available: - where_to_watch(title, country) for specific lookups - whats_new_on(provider, country) for "what's new on Netflix India this month" type queries - search_titles(q, country) for ambiguous queries like "joker" Three things I learned building this: Tool descriptions are the killer feature. Claude picks the right tool based on the description alone; no orchestration prompt is needed. I list all 30 country codes and 24 provider slugs inline in the description, so Claude doesn't have to guess what's valid input. Thin-client architecture wins for distribution. The npm package is around 7 KB. All TMDB calls, caching (Next.js unstable_cache), and per-country logic live on my website backend behind 3 public JSON endpoints. End users install with one npx command, no API key. Trade-off: requires the website to be up, which is fine because the website IS the product. Links: - npm: https://www.npmjs.com/package/@ottasia/mcp-server - Source: https://github.com/AIweather-Anurag/ottasia-mcp-server - MCP: https://mcp.so/server/ottasia/Alweather-Anurag Asking for feedback, especially if you're in any of these 30 markets and want to stress-test the provider mapping. Drop a "where can I watch [show] in [country]" example as a comment, and I'll check what Claude returns vs what's actually available. submitted by /u/No_Being_2765 [link] [comments]

Claude web searches through Github taking a lot of tokens

i dont have a coder background just a few basic classes over 10 years ago. i understand the nature of claude searches for information through the web is a brute force approach i am wondering if you guys have any recommendation on how to reduce the token usage with my github? I do a lot of stock research and do a lot of research and was utilizing claude to do it. i build a research platform with claude and have kept the HTML and the data collected through claude interface but have moved it to github to try and keep using it from anywhere not just my computer. however, moving it from claude to github has exploded my token usage with the API. My goal is to keep pulling data on stocks to populate my dashboard i created and incorporate the latest and most accurate information. i have almost 100 stocks in my universe and pulling data through claude environment for 5 stocks was using about 20%-25% of my 5 hour limit which is fine i updated around 10 every 5 hours to leave room for my other uses, but now through github with the API instead, just 1 stock was using $1-$2 which is not economical. do you guys have any recommendations? submitted by /u/Mr_Guy121 [link] [comments]

I built a local context compiler so AI coding agents stop re-reading the same repo

I’ve been working on an open-source tool called Madar. The problem I kept running into with AI coding agents is that they often rediscover the same codebase again and again. They grep, read files, summarize, lose context, then repeat the same exploration in the next task. On larger TypeScript/Node.js repos, this becomes slow, noisy, and expensive in tokens. Madar tries to solve this by acting as a local context compiler. It builds a structural graph of your codebase, then compiles compact context packs for a specific task before the agent starts broad repo exploration. The idea is not to replace file search. It is to give the agent a better starting point: relevant files/symbols route/service/call relationships runtime execution slices source locations coverage/missing-context diagnostics compact prompts for agents It works locally and does not require an API key to build the graph. Current support is strongest for TypeScript/Node.js projects, with framework-aware extraction for things like NestJS, Next.js, Express, Fastify, Hono, tRPC, Prisma, and routing-controllers. It can be used through MCP with tools like Claude Code, Cursor, Copilot, and Gemini, or through CLI-generated prompts for tools like Codex, Aider, and OpenCode. The package was previously called graphify-ts, but I renamed it to: @lubab/madar Install: npm install -g @lubab/madar Basic usage: madar generate . --spi madar summary madar pack "how does auth work?" --task explain madar claude install I’ve also been testing it with native-agent benchmarks. In some real backend prompts, it reduced provider-reported input tokens significantly. I’m being careful with that claim because results depend heavily on the repo and task, but the direction is promising. What I’m trying to validate now: Is “context compilation” a useful layer for AI coding agents? Do execution slices make codebase explanations more reliable? Can we reduce token waste without hurting answer quality? What benchmark format would developers actually trust? GitHub: https://github.com/mohanagy/madar npm: https://www.npmjs.com/package/@lubab/madar I’d genuinely appreciate technical feedback, especially from people using Claude Code, Cursor, Copilot, Codex, Aider, or other coding agents on larger repos. submitted by /u/CaptainProud4703 [link] [comments]

ChunkHound v5.1

We shipped ChunkHound v5.0 + v5.1 recently and forgot to post about 5.0, so here’s the combined update. ChunkHound is a code search / code research tool for AI coding workflows, especially MCP-based setups with Claude Code, Codex-style agents, VS Code, etc. The big 5.x themes: - Multi-client MCP daemon: multiple MCP clients can share one DuckDB connection instead of fighting over locks - MCP search now returns token efficient markdown instead of JSON - More language support: Elixir, Dart, Lua, SQL, HTML/CSS/SCSS, and more - Better deep research support: OpenAI Responses API, Anthropic structured outputs, Grok, reasoning-effort controls - Safer indexing: global gitignore support, embedded SQL detection, disk usage limits, .env exclusion, and better handling of unknown file types A bunch of stability fixes around HNSW, WAL validation, DuckDB paths, MCP startup, Windows unicode, and parser install hints The goal is to make codebase context more reliable for real agent workflows: less lock contention, fewer indexing surprises, better search output for LLMs, and broader language coverage. Thank you so much for everyone who worked hard, reported bugs, and contributed to the project in one way or another. It wouldn't have been possible without you 🙏 submitted by /u/Funny-Anything-791 [link] [comments]

Spy-code: a local codebase graph tool for Claude AI coding workflows

I'm building an open-source tool called `spy-code` that turns a codebase into a queryable graph. It parses your repository using tree-sitter, extracts functions, classes, constants, calls, imports and references as nodes and edges, and stores the graph locally in SQLite. You can then query it via a CLI, GraphQL API, or a minimal MCP server. The goal is to give AI coding agents like Claude structured context about your codebase instead of just a pile of files. With `spy-code`, an agent can ask questions like "What functions call this?", "What changed since a certain commit?", or "Where is this interface implemented?" without scanning the whole repo. I'm looking for feedback from Claude AI users and agent builders: • How might this fit into your Claude coding workflows? • What kinds of graph queries would be most useful? • Are there Claude-specific use cases (e.g. code review, refactoring suggestions) that would benefit from a codebase graph? In our testing, using `spy-code` has reduced token usage wasted on searching and opening files by about 60%, and cut down AI hallucination-related deletions or breakages of business logic by roughly 82%. The project is open source: spy-code I'd appreciate any suggestions or critiques! submitted by /u/OwnEntrepreneur256 [link] [comments]

OpenAI Unethical Billing Practices

I had a $100 budget/month set on my OpenAI API organization. Despite that, OpenAI billed me almost $200. I had signed up for ChatGPT Pro and tried using the Codex App, but it was painfully slow/causing my computer to crash, so I switched to the Codex CLI. I did not realize it was still reading from my API key and not signed into ChatGPT, and I incurred almost $200 in API bills. I contacted OpenAI support and they refused to offer me any sort of refund or credit, even after reaching a human and multiple attempts. This seems really unethical: OpenAI provides no way to stop runaway API billing, and they refuse to refund customers who exceed their defined budget. The "budget" system does not actually stop spending, so it's entirely pointless. After searching extensively through the OpenAI API platform and documentation I see no way to limit your API spending. This is on top of me contacting them asking for a refund of ChatGPT Pro subscription a couple months ago after we had a newborn and I was unable to use it for that month. I forgot to cancel the auto-renew, but contacted them the same day of the renewal. They absolutely refused to give me any sort of refund. I've never had an organization refuse to refund subscriptions before when it was accidentally renewed. So I'm out $400 now, thanks openai. submitted by /u/Direct-Row9073 [link] [comments]

How-to: recover deleted conversations from Claude Desktop

DON'T PANIC (yet) Well, I just had an interesting few hours after somebody we will neither name nor describe deleted a very important conversation in Claude's Desktop app. Writing this up because frantic and somewhat sweaty searching did not find me any results, and Claude itself was also at a loss - perhaps a poor soul in the future will land here and appreciate a possible end to their woes. This requires some knowledge, just telling enough to get you a quick start. Once you have confirmed that you have in fact deleted Very Important Conversation and all the files within that you have inexplicably and due to no fault of your own neglected to download and backup, immediately fully exit Claude. Don't just press the X, go to File -> Exit or Menu -> Quit or some such. Use task manager or whatever equivalent on your OS and make sure it is no longer running. Check again. Then realize as we all must that the Claude Desktop app is just Temu Chromium, and aggressively caches data - including virtually everything received from Claude servers. Find your cache directory (YMMV): Windows: %APPDATA%\Claude\Cache\Cache_Data\ macOS: ~/Library/Application Support/Claude/Cache/Cache_Data/ Linux: ~/.config/Claude/Cache/Cache_Data/ These will contain files such as index, data_0/1/2/3, and f_*. You can filter the f_* files by today's date to save you some hassle. Copy these files to a backup location. In that backup location, create and activate a Python virtual environment, and launch Claude Code CLI. Tell it something like this: ``` Claude, I have made a boo-boo and deleted very important stuff from my Claude Desktop install, I need you to help recover it! Please make sure the local Python environment has packages to handle brotli, zstd, gzip, deflate and zip file decompression. Additionally, install the ccl_chromium_reader package. If pip doesn't have it, "git clone https://github.com/cclgroupltd/ccl_chromium_reader.git" and install that local copy into pip. Explore the package's API; the module you want is ccl_chromium_cache. Use this parsing package, the blockfile cache has framing around compressed bodies. We are trying to recover conversations and files from a Chromium blockfile cache. The cache files (index, data0..data_3, f*) are in this directory. Use the ccl_chromium_reader package to walk every cache entry, decompress the HTTP body, and write each response to its own file in a new "extracted" subdirectory. Flag any entry whose decoded bodies contains . ``` You get the idea. If you have the conversation's UUID, use it as search phrase. When done, you'll have an extracted/ folder of JSON, HTML, and binary files. Your conversation is in whichever response body has a URL containing /chat_conversations/ . Generated artifacts (docx/pdf) come out as their own files. You can further prompt Claude Code to explore/extract whatever you want. Of course, this is cache, so it's not all guaranteed to still be there (so do this immediately when you lose it) but I successfully recovered everything that got lost, including all the documents inside the lost chat. If you used the claude.ai website instead of the app, a similar process may work by closing Chrome and finding its Cache_Data directory. The format is the same. But your data is probably a little harder to find. Many thanks to cclgroupltd. Should be enough to get anyone started. submitted by /u/Bootrear [link] [comments]

Anthropic officially launched 13+ FREE AI courses with certificates (Including Agentic AI and Claude Code!)

Just found out about this and had to share because almost nobody is talking about it yet. If you are tired of paying for AI courses or getting hit with paywalls just to get a certificate, Anthropic (the creators of Claude) quietly dropped a massive library of completely free, official training modules. Yes, they actually give you an official certificate of completion directly from Anthropic once you finish. Here is the breakdown of what is available and exactly how to get it without spending a dime. What is in the course catalog? They have split the training into a few different paths depending on what you want to do: The Big Surprise: Agentic AI & MCP: They have official courses on the Model Context Protocol (MCP). This is the cutting-edge tech used to build AI Agents that can browse your local computer, use tools, and execute tasks autonomously. Claude Code 101: Dedicated developer modules for their new command-line agent. It teaches you how to let Claude edit your codebase, run tests, and use its new "Plan Mode." API & Cloud Architecture: Deep dives into building with the Claude API, plus corporate tracks for deploying Claude securely inside Amazon Bedrock and Google Cloud Vertex AI. Everyday Productivity: If you aren't a coder, they have "Claude 101" and "AI Fluency" tracks. These teach advanced prompting, managing Projects, and using Artifacts for daily work. How to access it for free Anthropic hosts these courses on their official training academy platform (built on Skilljar). Because I can't post direct links here, here is how you find it: Search Google for "Anthropic Skilljar Academy" or "Anthropic Skilljar Catalog". Click the official link pointing to the Anthropic Skilljar domain. Sign up for a free account. You do not need to enter any credit card info. Choose your track, complete the lessons, pass the quick review quizzes, and download your certificate. Alternative Free Options If you want interactive coding environments alongside your videos, CodeSignal also has a free partnership track called "Developing Claude Agents" in Python and TypeScript that grants free certificates upon passing their labs. Go grab these before they decide to gate them behind a paywall! submitted by /u/Specialist_Engine522 [link] [comments]

GitHub’s Fake Engagement Problem Is Hiding in Plain Sight

Turns out: very visible. Yesterday's scan found 185 out of 185 engagers on a single repo were bots. Not 90%. Not "mostly suspicious". Every single one. The repo had zero legitimate stars. What I built phantomstars is a Python tool that runs daily via GitHub Actions (free, no servers): Scrapes GitHub Trending and searches for repos created in the last 7 days with sudden star spikes Pulls star and fork events from the last 24 hours per repo Bulk-fetches every engager's profile via the GraphQL API (account creation date, follower counts, repo history) Scores each account on a weighted model: account age (35%), profile completeness (30%), repo patterns (25%), activity history (10%) Detects coordinated campaigns using timestamp clustering and union-find: groups of 4+ suspicious accounts that engaged within a 3-hour window Files an issue directly on the targeted repo so the maintainer knows what's happening Campaign IDs are deterministic SHA-256 fingerprints of the sorted member set, so the same group of bots gets the same ID across runs. You can track a farm across multiple days even as individual accounts get suspended. What the pattern actually looks like It's remarkably consistent. A fake engagement campaign in the raw data: 40-200 accounts, all created within the same 1-2 week window Zero original repositories, or only forks they never touched No bio, no location, no followers, no following All of them starring the same repo within a 90-minute window The target repo usually has a name implying it's a tool, hack, executor, or generator Today's scan: 53 active campaigns across 3,560 accounts profiled. 798 classified as likely_fake. The repos being targeted are mostly low-quality AI tools and "executor" software that needs manufactured credibility fast. Notifying the affected repo When a repo hits a 40%+ fake engagement ratio or a campaign is detected, phantomstars opens an issue on that repo with the full suspect table: account logins, creation dates, composite scores, campaign membership. The maintainer sees it in their own issue tracker without having to find this project first. Worth noting: a lot of these repos have issues disabled, which is a red flag on its own. Those get skipped silently. Why I built this Stars are how developers decide what to evaluate, what to depend on, what to recommend. When that signal is bought, it affects real decisions downstream. This started as curiosity about how measurable the problem was. The answer was more measurable than I expected. It's part of broader research into AI slop distribution at JS Labs: https://labs.jamessawyer.co.uk/ai-slop-intelligence-dashboards/ The fake engagement problem and the AI content quality problem are really the same problem. Fake stars are the distribution layer that gets garbage in front of real users. All open source. The data is append-only JSONL committed back to the repo after every run, queryable with jq. Repo: https://github.com/tg12/phantomstars Findings are probabilistic, false positives exist, the README explains the full scoring model. If your account shows up and you're a real person, there's a false positive process. Questions welcome on the detection approach, GraphQL batching, or campaign ID stability. submitted by /u/SyntaxOfTheDamned [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]

Honest Response From Claude

This should be our work around when working with any AI model. we know these but we always miss these. hope this helps for many these are the basics submitted by /u/B_Ali_k [link] [comments]

I built a Chrome extension that gives your AI coding tools a memory layer - took 3 months, Claude helped me ship it.

I built Herb • - a productivity layer that sits on top of your AI coding tools. Honestly, probably 60% of the actual coding happened in Claude. I'd describe the feature, Claude would write the logic, I'd test it, break it, come back and fix it. That loop for 3 months. It's a weird kind of collaboration but it works. You know how every time you open a new Claude or ChatGPT chat, it has no idea who you are? You have to explain yourself every single time. "I'm using Next.js, TypeScript, Tailwind, here's what I'm building, here's how I like my code structured..." - same thing, every session, every tool. Herb • fixes that. You write it once. Every new chat remembers it. That's the core. What Herb does: Context Injection - set up a profile once (stack, preferences, current goals). Inject it into any AI chat in one click. No retyping your setup every session. Rules Library - save your .cursorrules and prompting patterns. Tag, search, copy in one click. Session History - save AI conversations with a button that appears on Claude and ChatGPT. Reference them later. Projects - group rules and sessions by project across tools. Prompt Templates - reusable templates with variables like {{language}} or {{error_message}}. Fill and fire. Community Rules - shared library of production rules anyone can import. Next.js, FastAPI, React TypeScript, Tailwind, Node/Express. You can contribute yours too. It's free. And I would genuinely love honest feedback after using the tool. Herb • Chrome Extension submitted by /u/Opening-Fun-7280 [link] [comments]