Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
Anthropic's main strength lies in its advanced AI model, Claude Opus 4.6, which supports extensive tasks like building a C compiler with a massive 1M token context window. However, users commonly complain about the significant rise in API costs associated with these advanced capabilities, leading to dissatisfaction with its pricing. Pricing sentiment is generally negative due to cost increases and limited usage options for the price point, such as the $200/month plan allowing only five daily prompts. Despite these concerns, Anthropic maintains a strong reputation for pushing AI innovation, although there are hints of financial strain noted in some discussions.
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Anthropic's main strength lies in its advanced AI model, Claude Opus 4.6, which supports extensive tasks like building a C compiler with a massive 1M token context window. However, users commonly complain about the significant rise in API costs associated with these advanced capabilities, leading to dissatisfaction with its pricing. Pricing sentiment is generally negative due to cost increases and limited usage options for the price point, such as the $200/month plan allowing only five daily prompts. Despite these concerns, Anthropic maintains a strong reputation for pushing AI innovation, although there are hints of financial strain noted in some discussions.
Features
Use Cases
Industry
research
Employees
4,700
Funding Stage
Series G
Total Funding
$57.7B
42,321
GitHub followers
78
GitHub repos
3,058
GitHub stars
20
npm packages
2
HuggingFace models
17,057,349
npm downloads/wk
OpenAI’s Game-Changing o1 Description: Big news in the AI world! OpenAI is shaking things up with the launch of ChatGPT Pro, priced at $200/month, and it’s not just a premium subscription—it’s a glim
OpenAI’s Game-Changing o1 Description: Big news in the AI world! OpenAI is shaking things up with the launch of ChatGPT Pro, priced at $200/month, and it’s not just a premium subscription—it’s a glimpse into the future of AI. Let me break it down: First, the Pro plan offers unlimited access to cutting-edge models like o1, o1-mini, and GPT-4o. These aren’t your typical language models. The o1 series is built for reasoning tasks—think solving complex problems, debugging, or even planning multi-step workflows. What makes it special? It uses “chain of thought” reasoning, mimicking how humans think through difficult problems step by step. Imagine asking it to optimize your code, develop a business strategy, or ace a technical interview—it can handle it all with unmatched precision. Then there’s o1 Pro Mode, exclusive to Pro subscribers. This mode uses extra computational power to tackle the hardest questions, ensuring top-tier responses for tasks that demand deep thinking. It’s ideal for engineers, analysts, and anyone working on complex, high-stakes projects. And let’s not forget the advanced voice capabilities included in Pro. OpenAI is taking conversational AI to the next level with dynamic, natural-sounding voice interactions. Whether you’re building voice-driven applications or just want the best voice-to-AI experience, this feature is a game-changer. But why $200? OpenAI’s growth has been astronomical—300M WAUs, with 6% converting to Plus. That’s $4.3B ARR just from subscriptions. Still, their training costs are jaw-dropping, and the company has no choice but to stay on the cutting edge. From a game theory perspective, they’re all-in. They can’t stop building bigger, better models without falling behind competitors like Anthropic, Google, or Meta. Pro is their way of funding this relentless innovation while delivering premium value. The timing couldn’t be more exciting—OpenAI is teasing a 12 Days of Christmas event, hinting at more announcements and surprises. If this is just the start, imagine what’s coming next! Could we see new tools, expanded APIs, or even more powerful models? The possibilities are endless, and I’m here for it. If you’re a small business or developer, this $200 investment might sound steep, but think about what it could unlock: automating workflows, solving problems faster, and even exploring entirely new projects. The ROI could be massive, especially if you’re testing it for just a few months. So, what do you think? Is $200/month a step too far, or is this the future of AI worth investing in? And what do you think OpenAI has in store for the 12 Days of Christmas? Drop your thoughts in the comments! #product #productmanager #productmanagement #startup #business #openai #llm #ai #microsoft #google #gemini #anthropic #claude #llama #meta #nvidia #career #careeradvice #mentor #mentorship #mentortiktok #mentortok #careertok #job #jobadvice #future #2024 #story #news #dev #coding #code #engineering #engineer #coder #sales #cs #marketing #agent #work #workflow #smart #thinking #strategy #cool #real #jobtips #hack #hacks #tip #tips #tech #techtok #techtiktok #openaidevday #aiupdates #techtrends #voiceAI #developerlife #o1 #o1pro #chatgpt #2025 #christmas #holiday #12days #cursor #replit #pythagora #bolt
View originalPricing found: $0, $17, $200, $20, $100
| Model | Input / 1M tokens | Output / 1M tokens |
|---|---|---|
| claude-opus-4 | $15.00 | $75.00 |
| claude-sonnet-4 | $3.00 | $15.00 |
| claude-4-opus | $15.00 | $75.00 |
| claude-4-sonnet | $3.00 | $15.00 |
| claude-3.5-sonnet | $3.00 | $15.00 |
| claude-3.5-haiku | $0.80 | $4.00 |
| claude-3-opus | $15.00 | $75.00 |
| claude-3-haiku | $0.25 | $1.25 |
Light
1M tokens/mo
$0.65 – $39
claude-3-haiku → claude-opus-4
Growth
50M tokens/mo
$33 – $1,950
claude-3-haiku → claude-opus-4
Scale
500M tokens/mo
$325 – $19,500
claude-3-haiku → claude-opus-4
Estimates assume 60/40 input/output ratio. Actual costs vary by usage pattern.
Claude makes documents into apps
# Any document can become an app I’ve been working on an open-source document format and viewer called **Adaptive Markdown**. The basic idea is simple: A document should not have to stay static. It should be something a coding agent can extend, reshape, and turn into an interactive workspace. This is not just a canvas you edit with a chatbot. The bigger idea is that the document becomes both: 1. the source of truth 2. the programmable interface In other words, the document becomes a living app. You write notes, collect data, draft text, or import files. Then a coding agent can directly modify the document surface: add charts, create calculators, build filters, restyle sections, generate summaries, export views, or turn rough notes into an interactive tool. So instead of having: * a document * a spreadsheet * a dashboard * an app * a changelog * a separate AI chat about all of it You can have one living `.md` file that contains those layers together. # Example A fitness log might start as a plain Markdown journal. Then the agent adds charts. Then it pulls in device data. Then it adds weekly summaries, rolling averages, goal tracking, export options, and a dashboard view. The document did not move into an app. The document became the app. # Other use cases * A billable time log that computes subtotals and rewrites rough notes into polished narratives * A research notebook with experiment parameters, runnable code, outputs, and methodology notes * A recipe book that scales servings and generates shopping lists * A math textbook that can explain a theorem at different levels * A project README that explains the system, demonstrates the system, and lets the agent modify it from inside the document * A small data report with embedded CSV data, live charts, filters, and exportable views The thing I’m most interested in is not "Can Markdown support more widgets?" It is: **What happens when the document itself becomes the programmable, agent-editable interface?** # Demos I made a few short video demos: * Turn your document into a snake game: [https://youtu.be/l-I2UiZd-Jw](https://youtu.be/l-I2UiZd-Jw) * Basic Adaptive Markdown features: [https://youtu.be/cLdzvZAL96I](https://youtu.be/cLdzvZAL96I) * Import CSV, create tables, edit and format them: [https://youtu.be/XKh9D3BlTCg](https://youtu.be/XKh9D3BlTCg) * Import MusicXML and transpose sheet music: [https://youtu.be/8YV3zjMLvA8](https://youtu.be/8YV3zjMLvA8) # Why I’m excited about this The biggest use case I’m excited about is academic and technical reading. In a few years, I don’t think people will just read papers passively. I think they’ll translate passages, ask questions, generate examples, explore alternate proofs, run code, attach notes, convert math to Lean where possible, and keep all of that inside the document instead of scattered across chats and notebooks. This is already pretty natural inside a browser when a coding agent has access to JS, CSS, and the document structure. It’s very early, but the workflow already feels useful to me. I’m using it for my own notes and documents. Right now it is configured for the Anthropic coding-agent SDK and experimentally for Codex. The longer-term goal is to make it run entirely locally. GitHub: [https://github.com/SemiSimpleMath/Adaptive-Markdown](https://github.com/SemiSimpleMath/Adaptive-Markdown) I recently added per-document skills, so agents can automatically know how to style or transform the text or data inside a specific document. Curious whether this seems useful to anyone else, or whether I’m just overexcited because I built it. Feature requests welcome.
View originalBuilding the harness around our coding agents: eight failure modes, eight pillars
We ended up building two products: the software we ship, and the system/harness around our agents that makes them useful in building the thing we ship. A harness is the durable layer around a model: instructions, tools, permissions, context, and verification. Claude Code and Codex are harnesses in this sense. Each wraps a model with a system prompt, a tool surface, a permission model, and an execution loop. Anthropic and OpenAI own that layer. We own the next layer up: the workspace where agents do product work alongside us, with our files, tasks, diagrams, diffs, and decisions. This layer carries the knowledge we have accumulated: how we build things, what we already decided, what is connected to what, where the agent is allowed to act, and how it checks its own work. We identified eight coding agent failure modes that kept showing up across our sessions. Each one got its own pillar that we are continuing to invest in: * Doesn't know our codebase, rules, decisions, or conventions → **Context** * Can't traverse the links between artifacts that already exist → **Provenance** * Can't act on the world or observe what it did → **Capability** * Reinvents how to do every task → **Workflow** * Does something dangerous because nothing stops it → **Restraint** * Hallucinates "fixed" without proof → **Verification** * Can't show results back to us in a useful form → **Visual interface** * We can't keep track of work happening across many agents in parallel → **Coordination** For example, with Verification. The agent hallucinates "fixed" without proof . We write the failing test before writing the fix, so the bug has a reproduction the next agent can rerun. If the agent cannot show the change works end-to-end, it is not done. Or the agent works for hours and "fixes" the solution while breaking 2 other things or re-architecting 3 subsystems. We require full test case completion. The full writeup with diagrams and links to our actual harness dot md is in the comments. What other coding agent failure modes / harness pillars are you addressing for yourself / team and how?
View originali benchmarked Anthropic's tool-search-tool head to head against our own MCP gateway on Opus 4.7. ours held up noticeably better
i'd been running Claude Code with a long list of MCP servers connected. Linear, Notion, GitHub, Slack, a few internal ones. and i was pretty confident that Opus 4.7 plus Claude Code's built in tool-search-tool would just absorb all of it. it mostly did. but i was still hitting \~20% context saturation way too often, before doing any actual work. tried Ratel (our own MCP gateway, we built it for exactly this problem) kind of out of curiosity. then we benchmarked it properly, head to head against Anthropic's own tool-search-tool, same model (Opus 4.7), realistic tool catalogs at 50 / 100 / 180 tools. at the 180 tool pool, measured against the full-catalog baseline: * Ratel: near parity on accuracy (about -1.7pp) and roughly -81% input tokens. * Anthropic's tool-search-tool: about -8.4pp accuracy. so somewhere around 5x the accuracy hit, same model, same catalog. the takeaway for me: a big context window and a built in tool search are not the same thing as a gateway thats actually optimised for the one job of deciding what enters context. repo plus the full benchmark, numbers and methodology, is here: [github.com/ratel-ai/ratel](http://github.com/ratel-ai/ratel) happy to be wrong on parts of this. if you run it differently and get other numbers id genuinely want to see them.
View originalIs it only me who finds Claude extremely acerbic compared to others?
Is it only me who finds Claude extremely acerbic compared to others?
View originalMade an awesome-list for everything LLM cost, would love contributions
So a few months back I got surprised by my Anthropic bill which somehow racked up like $400 ish on a staging key in a few weeks just running evals, no budget cap pretty dumb in hindsight I mean it’s not a big cost but I should have been careful nonetheless After that I started keeping a notes file of tools that actually helped reduce cost stuff like token counters, pricing pages that update properly, caching layers, prompt compression libs, observability tools (helicone, langfuse, langsmith, etc) it slowly grew to 80–90 entries so I cleaned it up and put it on github: [https://github.com/ankitvirdi4/awesome-llm-cost](https://github.com/ankitvirdi4/awesome-llm-cost) what’s in there right now: pricing calculators + token counters observability / tracing (helicone, langfuse, langsmith, openllmetry, phoenix) caching (gptcache, semantic caching approaches) model routers (openrouter, notdiamond, portkey) prompt compression + context window stuff eval cost tracking self hosting / GPU cost calculators everything is linted (awesome-lint), short descriptions for each entry, and I checked links recently so nothing should be dead if there’s anything you’ve used that saved you money on inference, drop it here or send a PR especially looking for more prompt compression stuff, that section feels kinda weak rn not affiliated with anything listed btw just got tired of having 80 bookmarks
View originalAnthropic vs OpenAI - which one will prevail by 2030
As the title suggests, I am trying to understand where people sit between these two companies. Which one do you think will still exist by 2030? If both of them, then which one will win the AI race? [View Poll](https://www.reddit.com/poll/1to26v9)
View originalJust passed the new Claude Certified Architect - Foundations (CCA-F) exam with a 985/1000!
The original post was removed by Reddit Filters, so I made new one with same content. I just got my results back today and managed to snag the Early Adopter badge as well. Following up on my recent DP-600 certification, I really wanted to validate my architecture skills specifically on the Anthropic side. The exam covers a lot of practical ground on prompt engineering for tool use, managing context windows efficiently, and handling Human-in-the-Loop workflows. Link to join: https://anthropic.skilljar.com/claude-certified-architect-foundations-access-request Training courses: https://anthropic.skilljar.com/ Cookbook: https://github.com/anthropics/anthropic-cookbook I've created my own Playbook and Mock Exam after the exam: https://drive.google.com/file/d/1luC0rnrET4tDYtS7xe5jUxMDZA-4qNf-/view?usp=sharing https://claude-certified-architect-mock-exam-cyberskill.vercel.app If anyone is preparing for this right now and has questions about the format or the types of architectural patterns tested, ask away! Happy to share some insights on what to study. Updated 26th May 2026: I noticed some mates treated me bananas (https://buymeacoffee.com/zintaen), didn't expect that, but you made my day. I'll use that fund to take more CERTs and create a site for mock tests (always free, of course). Thanks again.
View originalClaude now has Pope's blessing
If you don't know what's the signifigance of "Magnifica humanitas", ask Claude for more - in short it is the highest form of papal (Pope's) teaching. Virtually never accompanied by commercial parties. The first one usially sets the tone of current Pope's career. Now they had Anthropic's Christopher Olah there, chilling with the Pope and addressing the humanity. While not official, in practice and in eyes of people & religious communities, Anthropic is now "the chosen one". Pope's stuff: https://www.vaticannews.va/en/pope/news/2026-05/pope-leo-xiv-encyclical-magnifica-humanitas-ai.html Anthropic's stuff: https://www.anthropic.com/news/chris-olah-pope-leo-encyclical
View originalWeird Injection Prompt In Chat??
Claude inserted an injection prompt at the end of its message out of the blue, and i have repeatedly asked where it got it from or why it inserted this message, but Claude keeps denying it ever did it, no matter how many screenshots or replies i use or whatever i do, Claude just purely denies it and it went as far as saying there could be a physical sticker on my screen but wont accept saying this I am a uni student studying for an exam in 2 days, and I'm 19, so I don't understand
View originalBuilt a Claude Meeting Assistant Plugin
I had the itch to build something… works great for me so sharing in case someone else here can benefit. Built with claude, for claude. And yes, it's free. my entire job (product manager) is constantly referencing every context channel we have (slack, emails, CMS, Github, Linear, etc.) --> scoping features, resource planning, digging up those tiny details the stakeholders mentioned they needed… Claude works great as my command center with all the connectors. But the most critical juncture of needing all this is **IN** my team meetings. **what I tried**: * Granola, Firefly, etc: all just notetakers, no actual in-meeting action * Gemini: our team is on Claude/Claude Code, it’s what everyone is used to, and can’t afford another company AI subscription * Meeting participant bots: a bot having its own participant window felt intrusive and like we were being watched * Claude but outside the meeting: our team is entirely remote and I need our team present during these meetings. I am strongly against having other tools open during meetings unless we absolutely have to. **my solution**: * I created a Claude plugin that lets me dial-in my Claude, so I can have all **my** MCP’s, skills, connectors, and context available in the chat panel of the meeting, available to the whole team * No more I’ll check and we can schedule a follow-up * No more spending meeting time looking something up * No more list of misc to-do’s post-meeting * Everything can be ascertained and delegated in the meeting, by all participants so meetings are actually productive and everyone leaves with zero tedious follow-ups **features:** * Claude can reference both what was discussed in the current meeting as well as chat messages live + historical records of meetings of course * Two modes: **DIAL** which is where you can "@claude" in the chat panel to ask/delegate and **WIRETAP** which is just recording meeting + chat messages * Everything is spawned directly from wherever you Claude Code - meaning your chat before you dial in claude gets loaded in as context (I typically set an agenda/reminders or just use it for prep) and after the meeting you can debrief/recap in the very same chat session * Meeting data lives on your machine and your machine only * Yes, it uses your subscription and **NOT** the API; we are within anthropic’s TOS here. Just had to be creative about it **limitations:** * Claude replies under your name but with a visible prefix (see demos below) * The plugin opens its own version of a chrome browser to get Claude in there with you FYI * Mac only — linux/windows next * Google meet only — teams/zoom next * Claude only — I want to add codex, openclaw, and local LLMs next How it's going for us now... we got rid of our Granola subscription which we love but was getting costly for us, and I just want less UI’s in my life tbh. So it’s worked great for us so far. Some demos below - give it a spin and give me some feedback if you want! GitHub repo: [https://github.com/1-800-operator/operator/fork](https://github.com/1-800-operator/operator/fork) **quickstart run in terminal**: `# 1. One-line install — sets up the / slash commands` `curl -fsSL` [`1-800-operator.com/install`](http://1-800-operator.com/install) `| bash` `# 2. Open Claude Code and type:` `/dial` [`https://meet.google.com/xxx-yyyy-zzz`](https://meet.google.com/xxx-yyyy-zzz) `# 3. Go further — more slash commands:` `/dial-yolo <meet-url> # no asks, full speed` `/wiretap <meet-url> # just record, no bot` https://i.redd.it/qp998satxc3h1.gif https://i.redd.it/afjsve8yxc3h1.gif
View originalMultiple AI assistants are hallucinating official Discord invites — this is a phishing risk, not a normal hallucination
I think this is a serious AI safety/security issue: multiple AI assistants appear to hallucinate or confidently endorse “official” Discord invite links for Anthropic/Claude. I’m intentionally not posting the exact invite strings here because I don’t want anyone clicking or testing random Discord invites from a Reddit post. But people can reproduce the issue themselves by asking different AI assistants for the official Anthropic/Claude Discord and checking whether they give direct Discord invite links instead of telling users to verify only through Anthropic’s official website. What I observed: One assistant confidently gave me a direct invite and presented it as the official Anthropic Discord. Another answer gave a different “official” invite with the same confidence. Some answers referenced third-party-looking sources or invite directories instead of treating Anthropic’s own website as the only acceptable authority. Even Claude-related answers can fall into this pattern. This is not a harmless hallucination. Discord invite links are a high-risk phishing surface. Fake “official” servers can copy branding, use fake verification bots, impersonate support/community channels, and push users toward wallet-drainer flows, malicious approvals, credential phishing, or malware. The core problem is confidence. These assistants do not reliably say “verify this through the official company website.” They can present generated or third-party invite information as if it were verified. For security-sensitive contexts like official communities, Discord invites, crypto wallets, verification bots, and support channels, AI assistants should follow a stricter policy: Do not guess Discord invites. Do not autocomplete “official” community links. Do not rely on third-party invite directories. Do not present generated Discord invite strings as verified. Send users only to the organization’s official website and tell them to navigate from there. Warn users not to trust invite links from AI-generated text, DMs, social media, YouTube descriptions, GitHub issues, or third-party pages. This should be treated as a security failure, not just a factual error. A confident wrong answer here can send users directly into a phishing funnel and cause real harm.
View originalANTHROPIC 🔥: Mythos 1, "claude-mythos-1-preview", is being prepared for a release on Claude Code and Claude Security.
The model became visible for a short amount of time on Claude; besides that, new strings mentioning Mythos have been added. \> Access to the Claude Mythos model in Claude Code and Claude Security. It still doesn't mean the general public will have access to this exact model, according to Anthropic's earlier communication.
View originalAnthropic moves closer to powering America's spy agencies
submitted by /u/Goldenmentis [link] [comments]
View originalMy Mac now has a wake word for Claude Code
Honestly this started as a weekend hack because I was tired of typing the same kind of prompts into Claude Code over and over. I wanted to just talk to it while making coffee. So I rigged up a wake word (Yabby), a WebRTC voice loop for the conversation, and an actual plan-approval modal that pops up before any agent runs so I can vet what's about to happen first. That was the plan. Two weekends later it had quietly turned into something weirder. The voice loop now talks to a "lead agent" that breaks the work down into a discovery phase, a plan, then it recruits a small team a manager or two, and sub-agents that actually do the work. They run in parallel where they can, sequentially where they can't, and when a sub-agent finishes there's an auto-triggered review pass (5 second debounce so they don't pile up). The lead agent watches the whole cascade and reports back by voice when everything's QA'd and done. Each agent runs its own Claude Code session under the hood with its own thread, so the conversations don't bleed. Watching three agents work in parallel on the same project last night was genuinely uncanny. One of them caught a bug another one had written. That part I really didn't expect. Things I still hate about it: - Speaker verification is fiddly. Cosine-similarity threshold on the speaker embedding is annoying to tune too tight and it rejects me when I have a cold, too loose and it'll wake for anyone in the room. - French was the default locale because I wrote it that way. Slowly fixing it. - Background tasks dying when the parent Claude Code CLI exits was a nightmare to track. Ended up writing an OS-level PID watcher with a bookkeeper shell script just to know which long-lived servers had crashed. - Lead agent occasionally over-plans tiny tasks. Ask it to rename a file and you get a four-phase project plan. Working on it. Stuff I'm still figuring out: how to make the QA phase less chatty, whether to let sub-agents recruit their own sub-agents, and how to keep the voice latency under 300ms when the Realtime API gets cranky. Curious if anyone else has tried voice-controlling Claude Code? Anthropic rolled out their own voice mode to 5% of users a couple weeks back and I keep wondering how they'll handle the multi-agent piece does anyone here have access to that rollout yet? submitted by /u/Interesting-Sock3940 [link] [comments]
View originalPSA: Claude Code silently loses session data. Here is a backup script for Windows & Mac
The Problem If you've been using Claude Code (the CLI / desktop app) and noticed sessions vanishing — you're not alone. The title stays in the sidebar but clicking it shows nothing. The transcript is gone. No warning, no error, no recovery option. This has been reported by multiple users. It seems to happen silently — possibly during context compression, unexpected exits, or some storage-layer issue. There's no built-in backup or recovery feature. For a paid product, this is a pretty rough experience. You build up a long session with real work in it, and it just disappears. The Fix: Daily Automated Backups Since Anthropic hasn't addressed this yet, I built a simple daily backup that runs completely independently of Claude Code via your OS scheduler. It copies all session transcripts, plans, drafts, and memory to a safe location, keeps 7 days of rolling backups, and logs each run. No Claude dependency — if Claude crashes, gets uninstalled, or loses data again, your backups are still there. Windows (Task Scheduler + PowerShell) Step 1: Create the backup folder mkdir C:\Users\%USERNAME%\ClaudeBackups Step 2: Save this as backup-claude-sessions.ps1 in that folder $ErrorActionPreference = "Stop" $source = "$env:USERPROFILE\.claude" $backupRoot = "$env:USERPROFILE\ClaudeBackups" $logFile = Join-Path $backupRoot "backup.log" $keepDays = 7 $timestamp = Get-Date -Format "yyyy-MM-dd_HHmmss" $backupDir = Join-Path $backupRoot $timestamp $dirs = @("sessions", "projects", "plans", "drafts", "memory") function Write-Log($msg) { $line = "$(Get-Date -Format 'yyyy-MM-dd HH:mm:ss') - $msg" Add-Content -Path $logFile -Value $line -Encoding utf8 } try { Write-Log "=== Backup started ===" New-Item -ItemType Directory -Path $backupDir -Force | Out-Null foreach ($d in $dirs) { $src = Join-Path $source $d if (Test-Path $src) { $dst = Join-Path $backupDir $d Copy-Item -Path $src -Destination $dst -Recurse -Force $count = (Get-ChildItem $dst -Recurse -File -ErrorAction SilentlyContinue | Measure-Object).Count Write-Log " Copied $d ($count files)" } else { Write-Log " Skipped $d (not found)" } } $size = (Get-ChildItem $backupDir -Recurse -File | Measure-Object -Property Length -Sum).Sum Write-Log " Total backup size: $([math]::Round($size/1MB, 2)) MB" # Rotate old backups $cutoff = (Get-Date).AddDays(-$keepDays) Get-ChildItem $backupRoot -Directory | Where-Object { $_.Name -match '^\d{4}-\d{2}-\d{2}_\d{6}$' -and $_.CreationTime -lt $cutoff } | ForEach-Object { Remove-Item $_.FullName -Recurse -Force -Confirm:$false Write-Log " Rotated old backup: $($_.Name)" } Write-Log "=== Backup completed successfully ===" } catch { Write-Log "!!! BACKUP FAILED: $_" exit 1 } Step 3: Save this as install-schedule.ps1 and run it once as Administrator $action = New-ScheduledTaskAction ` -Execute "powershell.exe" ` -Argument "-ExecutionPolicy Bypass -WindowStyle Hidden -File `"$env:USERPROFILE\ClaudeBackups\backup-claude-sessions.ps1`"" $trigger = New-ScheduledTaskTrigger -Daily -At 8:00AM $settings = New-ScheduledTaskSettingsSet ` -AllowStartIfOnBatteries ` -DontStopIfGoingOnBatteries ` -StartWhenAvailable Register-ScheduledTask ` -TaskName "ClaudeSessionsBackup" ` -Action $action ` -Trigger $trigger ` -Settings $settings ` -Description "Daily backup of Claude Code sessions" ` -RunLevel Limited Write-Host "Done! Runs daily at 8:00 AM." -ForegroundColor Green Run it: powershell -ExecutionPolicy Bypass -File "C:\Users\%USERNAME%\ClaudeBackups\install-schedule.ps1" Mac (launchd + shell script) Step 1: Create the backup folder mkdir -p ~/ClaudeBackups Step 2: Save this as ~/ClaudeBackups/backup-claude-sessions.sh #!/bin/bash set -euo pipefail SOURCE="$HOME/.claude" BACKUP_ROOT="$HOME/ClaudeBackups" LOG_FILE="$BACKUP_ROOT/backup.log" KEEP_DAYS=7 TIMESTAMP=$(date +"%Y-%m-%d_%H%M%S") BACKUP_DIR="$BACKUP_ROOT/$TIMESTAMP" DIRS=("sessions" "projects" "plans" "drafts" "memory") log() { echo "$(date '+%Y-%m-%d %H:%M:%S') - $1" >> "$LOG_FILE"; } log "=== Backup started ===" mkdir -p "$BACKUP_DIR" for d in "${DIRS[@]}"; do src="$SOURCE/$d" if [ -d "$src" ]; then cp -R "$src" "$BACKUP_DIR/$d" count=$(find "$BACKUP_DIR/$d" -type f | wc -l | tr -d ' ') log " Copied $d ($count files)" else log " Skipped $d (not found)" fi done size=$(du -sm "$BACKUP_DIR" | cut -f1) log " Total backup size: ${size} MB" # Rotate old backups find "$BACKUP_ROOT" -maxdepth 1 -type d -name "2*" -mtime +$KEEP_DAYS -exec rm -rf {} \; log " Rotated backups older than $KEEP_DAYS days" log "=== Backup completed successfully ===" Make it executable: chmod +x ~/ClaudeBackups/backup-claude-sessions.sh Step 3: Create the launchd plist to run daily at 8am Save this as ~/Library/LaunchAgents/com.user.claude-backup.plist: Label com.user.claude-backup ProgramArguments /bin/bash -c $HOME/ClaudeBackups/backup-claude-sessions.sh StartCalendarInterval Hour 8 Minute 0 StandardErrorPath /tmp/claude-backup-err.log RunAtLoad Loa
View originalRepository Audit Available
Deep analysis of anthropics/anthropic-sdk-python — architecture, costs, security, dependencies & more
Yes, Anthropic offers a free tier. Pricing found: $0, $17, $200, $20, $100
Key features include: Claude Opus 4.7, Claude is a space to think, Claude on Mars, Core views on AI safety, Anthropic’s Responsible Scaling Policy, Anthropic Academy: Build and Learn with Claude, Anthropic’s Economic Index, Claude’s Constitution.
Anthropic is commonly used for: Help and security.
Anthropic integrates with: Slack, GitHub, AWS Lambda, Google Cloud Platform, Microsoft Azure, Jupyter Notebooks, Trello, Zapier, Notion, Salesforce.
Anthropic has a public GitHub repository with 3,058 stars.
Chris Olah
Research Scientist at Anthropic
4 mentions

Introducing Claude Opus 4.6
Feb 5, 2026
Based on user reviews and social mentions, the most common pain points are: anthropic, claude, token usage, openai.
Based on 259 social mentions analyzed, 7% of sentiment is positive, 91% neutral, and 2% negative.