Accelerate software development with Amazon Q Developer — an AI coding assistant that writes, debugs, and refactors code natively in your IDE.
"Amazon Q Developer" receives consistently high ratings, indicating that users appreciate its robust capabilities, particularly in areas like log anomaly detection. Key strengths noted include its effectiveness and ease of deployment in AI-driven projects. However, there are frustrations with the rapid pace of updates, often leading to out-of-date documentation. Generally, users view the pricing as fair given its advanced features, while the overall reputation in the development community is positive, marked by frequent use in professional and personal projects.
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"Amazon Q Developer" receives consistently high ratings, indicating that users appreciate its robust capabilities, particularly in areas like log anomaly detection. Key strengths noted include its effectiveness and ease of deployment in AI-driven projects. However, there are frustrations with the rapid pace of updates, often leading to out-of-date documentation. Generally, users view the pricing as fair given its advanced features, while the overall reputation in the development community is positive, marked by frequent use in professional and personal projects.
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information technology & services
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1,560,000
‘Hyperscale’ data center project in Utah — expected to generate and consume more power than entire state
A massive **hyperscale data center project** in rural **Box Elder County, Utah**, led by Shark Tank investor Kevin O’Leary through his company O’Leary Digital (also known as the **Stratos Project** or **Wonder Valley**), is nearing final approval. The development, spanning about 40,000 acres of private land plus 1,200 acres of military and state-owned property, aims to host hyperscale data centers for tech giants like Amazon, Microsoft, and Google. It would generate its own power via natural gas from the Ruby Pipeline — starting at around 3 gigawatts in the first phase and scaling to 9 gigawatts at full buildout, exceeding Utah’s current statewide electricity consumption. Proponents highlight benefits including 2,000 permanent high-paying jobs, substantial tax revenue for Box Elder County (potentially $30 million initially, rising above $100 million annually), funding for modernization at Hill Air Force Base, and advanced water recycling technology that cleans and returns water to an aquifer feeding the **Great Salt Lake**, with minimal net usage. To attract the limited pool of hyperscalers, the Military Installation Development Authority (MIDA) has approved aggressive incentives, including slashing the energy use tax from 6% to 0.5%, significant property tax rebates (with 80% initially directed back to the developer), and personal property tax relief on rapidly depreciating equipment. The project still requires final sign-off from the Box Elder County Commission, which rescheduled its vote to Monday morning after commissioners expressed concerns about the rapid timeline and sought more resident input and legal review. O’Leary has praised Utah’s pro-business speed and framed the initiative as critical for U.S. competitiveness against China in AI and data infrastructure.
View originalPricing found: $19/mo, $.003, $0.003, $19, $19
g2
What do you like best about Amazon Q Business?Amazon Q helps to suggest the future code by analyzing your current code. It also helps in building logic and its implementations. Review collected by and hosted on G2.com.What do you dislike about Amazon Q Business?Its accuracy can be improved, but can be trained on more data . Review collected by and hosted on G2.com.
What do you like best about Amazon Q Business?I like Amazon Q because it enhances the terminal experience by adding a UI layer, making it incredibly easy to browse folders and files and autocomplete and suggest commands; even on terminals like iTerm2 (which is old compared to Warp or Hyper), and does it natural, smooth and responsive feeling as it was just extra native features of the terminal. The Ask AI feature is a game-changer—it’s perfect for quickly searching, troubleshooting, or getting suggestions for bash scripts and commands. This tool transforms the terminal into a powerful and intuitive workspace, making navigation and scripting much easier and more efficient. Review collected by and hosted on G2.com.What do you dislike about Amazon Q Business?I dislike only a few things. I don't like how often I need to re-authenticate into my account. Although it is easy to do, it can be frustrating sometimes. Review collected by and hosted on G2.com.
What do you like best about Amazon Q Business?I love using Amazon Q for generating my infrastructure as CDK code. It's also very helpful when generating code for any AWS services. Review collected by and hosted on G2.com.What do you dislike about Amazon Q Business?I don't like that it seems to generate outdated libraries or sometimes libraries that don't exist. Although, this is pretty common with most tools. Review collected by and hosted on G2.com.
What do you like best about Amazon Q Business?We are using Amazon Q Code Whisperer as a general code completion and generation tool. It is very easy to use both standalone and as a plugin for VSCode. The code completion feature is great and have a good understanding of full software project we are working on, as well as good insights on AWS-specific items. The AWS-specific comprehension is a major plus as it speeds up the development of Infrastructure as Code files. Review collected by and hosted on G2.com.What do you dislike about Amazon Q Business?While Amazon Q claims to be able to generate full project-level files, I personally found this feature to be incomplete. Given the right prompts it does, as promised, create all the needed files for the task, with a reasonable code-skill level, but it has been my experience that post-generation additions or fixes tend to break the existing code. Review collected by and hosted on G2.com.
What do you like best about Amazon Q Business?I really like how 'Amazon Q' helps in code suggestion and debugging. I can also select a piece of code ask it explain that code Review collected by and hosted on G2.com.What do you dislike about Amazon Q Business?Sometimes it doesn't understand the relationship between different components of code, present in different files Review collected by and hosted on G2.com.
Claude Fable 5 may return today after 13-day government-forced suspension
Here’s the full timeline: -June 9: Anthropic releases Claude Fable 5, their most powerful public model ever (Mythos-class with safeguards) -June 12: US government issues an export control directive at 5:21 PM, ordering Anthropic to cut off access to ALL foreign nationals. Model goes offline worldwide within 90 minutes -The reason? Amazon engineers reportedly found a narrow jailbreak that could bypass Fable’s cybersecurity classifiers -Anthropic complied but publicly pushed back, calling the action unfair -Trump met Dario Amodei at the G7 and softened his stance, but the directive was never officially lifted -June 26 (today): Congressional deadline for Commerce Secretary Lutnick to respond in writing about the export controls Prediction markets are pricing ~57% odds of restoration before July 1. Developers have been stuck on Opus 4.8 this whole time. This whole situation raises a serious question: if a government can pull your AI model offline in 90 minutes, what does that mean for anyone building on closed, hosted models? submitted by /u/Direct-Attention8597 [link] [comments]
View originalLaunching the Agentic AI World Cup — Design a multi-agent swarm visually to win up to $100
Hey everyone, Two months ago, We launched AgentSwarms to help developers learn and build POC using Agentic AI. Since then, over 3,800 learners have joined the platform. Now, it’s time to see what you can actually design when the gloves come off. This week, We're officially launching the Agentic AI World Cup. The twist? No complex boilerplate environment setup required. This competition is entirely focused on architectural design using the platform's visual canvas builder. 🏆 The Challenge Use the visual canvas builder to orchestrate a multi-agent swarm that solves a legitimate, real-world workflow problem. We want to see how creatively and robustly you can map out state transitions, routing logic, and multi-agent collaboration visually. 🎁 The Prizes 🥇 Winner — $100 Amazon Gift Card + Featured Spotlight on AgentSwarms 🥈 1st Runner-up — $50 Amazon Gift Card + Featured Spotlight on AgentSwarms 🥉 2nd Runner-up — $25 Amazon Gift Card + Featured Spotlight on AgentSwarms 📋 How to Enter Build & Publish: Open up the visual canvas builder on AgentSwarms. Design your multi-agent architecture and publish it to the Community with a detailed text write-up explaining your logic. Record & Submit: Record a quick video walkthrough of your visual swarm executing its workflow. Email a Google Drive link of the recording to hello@agentswarms.fyi. ⚖️ What the Judges Care About We are evaluating raw architectural design and execution logic: Problem Severity: Does this swarm solve a real, practical problem? Graph Logic: How clean and efficient is your visual routing and orchestration? Resilience: How well does your design handle edge cases or unexpected node outputs? Documentation: Is your community write-up detailed enough that someone else looking at your canvas can immediately understand the workflow? ⏱️ Deadlines Submission Deadline: July 10, 2026 Winners Announced: July 25, 2026 If you’ve been wanting to whiteboard a complex multi-agent system and actually see it run, this is the perfect sandbox to do it. If you have any questions and need any support drop us an email. submitted by /u/Outside-Risk-8912 [link] [comments]
View originalNobody’s talking about the real precedent in the Fable 5 ban: a nationality-based access rule that geography literally can’t enforce
TL;DR: Last Friday the US government ordered Anthropic to block all “foreign nationals” — including non-citizens inside the US — from using its new Fable 5 and Mythos 5 models. Since you can’t separate a green-card holder in California from a citizen in real time, Anthropic shut the models down for everyone. It’s the first time export controls have hit an AI model itself rather than the chips that run it. The under-discussed part: a nationality-based access rule that geography can’t enforce pushes companies toward building identity infrastructure — and your AI chats already have zero legal privilege. Even if this order gets reversed, the precedent is the story. What actually happened On June 12, the Commerce Department issued a national-security export-control directive ordering Anthropic to suspend access to Fable 5 (and the more powerful Mythos 5 it’s built on) for any foreign national — explicitly including non-citizens physically inside the US, down to Anthropic’s own employees. A source close to the company says it got ~90 minutes and no prior warning. Because Anthropic can’t filter foreign nationals from US users in real time, it disabled both models globally. The trigger, per WSJ, Axios, and Semafor reporting: a phone call from Amazon. Amazon CEO Andy Jassy reportedly told Treasury Secretary Scott Bessent and other officials that Amazon researchers had used Fable 5 to pull information useful for cyberattacks. That’s the same Amazon that’s Anthropic’s biggest investor (~$13B in, ~$20B more planned), its cloud and chip supplier, and a customer — and now the entity that got its own investment’s flagship product killed worldwide. Amazon won’t confirm details. At least five other companies reportedly called the administration that same window. The accounts conflict, which matters: • White House (via former AI czar David Sacks): a trusted partner found a real jailbreak, the administration asked Anthropic to patch or pull it, CEO Dario Amodei refused, so they acted “reluctantly” — and they want the model back once it’s fixed. • Anthropic: the “jailbreak” only surfaced a handful of already-known minor vulnerabilities that other public models like GPT-5.5 can find too, so recalling a model used by hundreds of millions is disproportionate. • A cybersecurity CEO who reviewed the findings said the research was defensive, not offensive. Why this is bigger than one model Export controls have hit AI chips for years. This is the first time they’ve hit a model itself. That reframes frontier models as controlled national-security assets — and it surfaces an enforcement problem nobody’s reckoning with. A normal “no users in Country X” rule is easy: geoblock by IP. But this rule covers foreign nationals inside the US. You cannot IP-block a French citizen sitting in San Francisco. So if a future order like this is meant to be enforced strictly — not “shut it all down,” but “keep serving Americans while genuinely excluding non-citizens” — there’s only one way to be certain who’s a citizen: verify identity. Self-attestation (“I certify I’m a US person”) shifts legal liability but provides zero actual certainty, because people lie. If the government’s bar is certainty, the only escape hatch from “go dark forever” is ID verification to access the model. That’s the precedent worth staring at: a category of rule whose strict form quietly makes “show ID to use AI” the path of least resistance. The part that’s already settled: your AI chats have no legal privilege This one isn’t speculative. In February, a federal judge in the Southern District of New York ruled that conversations with Claude carry no attorney-client privilege — Claude isn’t a lawyer, so the privilege can’t attach — and leaned on Anthropic’s own privacy policy stating users have no expectation of privacy in their inputs. Sam Altman has publicly admitted the same about ChatGPT. A separate ruling found ~20 million ChatGPT logs likely subject to compelled production, with users holding only a “diminished privacy interest.” (One Michigan judge went the other way, treating chats as personal work-product — so it’s trending bad, not fully locked in.) Now stack the two: AI access potentially gated to verified identities, and AI conversations that can be subpoenaed with no privilege. That’s a plausible near-future where using AI means an ID-linked, fully discoverable record of everything you ever asked it. The honest counterweights (so this isn’t catastrophizing) • The administration says it wants the model restored once the jailbreak is patched. The likeliest near-term outcome is the directive getting narrowed or pulled — not permanent ID walls. • Self-attestation is the historically normal compliance path for export-controlled software and doesn’t require collecting documents. • The last time the US tried to export-control software like this — strong encryption in the 1990s — the controls largely failed and were circumvented and relaxed rather than harde
View originalHi Reddit, I posted my Build Your Own LLM workshop which encourages Claude use for coding exercises
Hi internet friends, I recorded a workshop about building your own LLM without any math / ML prerequisites. It covers everything from machine learning fundamentals, deep neural networks, transformer architecture, and pre/post-training. AI-coding assistants like Claude/CC are often referenced and encouraged for coding exercises. The only prerequisite is being comfortable with learning through code & excel examples. Sampling Large Language Models Reverse Engineering Large Language Model Perceptrons: wx+b Activation Functions: ReLU, GELU, SwiGLU GPU Coding: PyTorch, torch.compile(), fused kernels, CUDA, Triton MLPs/FFNs: Multi-input, Multi-Layer Perceptrons, Feed-Forward Networks Loss Functions: Residual errors, RMSE, Cross Entropy, Loss Landscapes Backpropagation: Training loops, Optimizers, Learning Rate, Batch Size Saving & Loading Models Initialization: Kaiming, Glorot Residuals: Addition, Scaling, Gated, Concatenation Normalization: Pre-norm vs. Post-norm, RMSNorm, BatchNorm, LayerNorm Regularization: Dropout, Gradient Clipping, Weight Decay SoftMax Tokenizers: By Character, By Word, BPE, SentencePiece Embeddings: Absolute vs. Learned, Sinusoidal vs. RoPE Attention: MHA, GQA, MQA, MLA Transformers Pre-training: Data Sources, Datasets, HTML Cleaning, Quality Filtering, Sharding Evaluation: Leaderboards, Benchmarks, Verifiers vs LLM-as-Judge Instruction Tuning: Alpaca & Other Formats, Self Instruct, Capabilities Reinforcement Learning: Policy Optimization, SimPO What We Didn't Cover: Scaling Each section has slides teaching the concepts, followed by excel-by-hand developing intuition for the math, and then coding examples. The goal is able to grok all parts of modern LLM development. We did this workshop in-person in San Francisco last month and hopefully the spaciousness of watching online works for everyone. If don't like watching videos, you can get the slides and exercises and work self-paced. submitted by /u/JustinAngel [link] [comments]
View originalHi Reddit, I posted my Build Your Own LLM workshop to Youtube teaching how to rebuild OpenAI's GPT2-style Transformer
Hi internet friends, I recorded a workshop about building your own LLM without any math / ML prerequisites. By the end of the workshop people have their own working OpenAI GPT2-style transformer, which hopefully makes it relevant to this sub. The workshop covers everything from machine learning fundamentals, deep neural networks, transformer architecture, and pre/post-training. The only prerequisite is being comfortable with learning through code & excel examples. Sampling Large Language Models Reverse Engineering Large Language Model Perceptrons: wx+b Activation Functions: ReLU, GELU, SwiGLU GPU Coding: PyTorch, torch.compile(), fused kernels, CUDA, Triton MLPs/FFNs: Multi-input, Multi-Layer Perceptrons, Feed-Forward Networks Loss Functions: Residual errors, RMSE, Cross Entropy, Loss Landscapes Backpropagation: Training loops, Optimizers, Learning Rate, Batch Size Saving & Loading Models Initialization: Kaiming, Glorot Residuals: Addition, Scaling, Gated, Concatenation Normalization: Pre-norm vs. Post-norm, RMSNorm, BatchNorm, LayerNorm Regularization: Dropout, Gradient Clipping, Weight Decay SoftMax Tokenizers: By Character, By Word, BPE, SentencePiece Embeddings: Absolute vs. Learned, Sinusoidal vs. RoPE Attention: MHA, GQA, MQA, MLA Transformers Pre-training: Data Sources, Datasets, HTML Cleaning, Quality Filtering, Sharding Evaluation: Leaderboards, Benchmarks, Verifiers vs LLM-as-Judge Instruction Tuning: Alpaca & Other Formats, Self Instruct, Capabilities Reinforcement Learning: Policy Optimization, SimPO What We Didn't Cover: Scaling Each section has slides teaching the concepts, followed by excel-by-hand developing intuition for the math, and then coding examples. The goal is able to grok all parts of modern LLM development. We did this workshop in-person in San Francisco last month and hopefully the spaciousness of watching online works for everyone. If don't like watching videos, you can get the slides and exercises and work self-paced. submitted by /u/JustinAngel [link] [comments]
View originalFor those, who believed another reset is coming anyway
there is no another reset, that's the last one ps https://chatgpt.com/share/6a1c7996-d54c-8322-89c2-600ab96165c7 submitted by /u/nikanorovalbert [link] [comments]
View originalAnthropic casually said mythos is coming to all customers in the coming weeks and buried it at the bottom of the opus 4.8 announcement
Anthropic buried the biggest news of the year inside a routine model update announcement The opus 4.8 launch today is fine solid incremental improvement, 4x fewer unflagged code flaws, dynamic workflows, effort controls and same pricing. cool cool cool then at the bottom we r making swift progress on developing these safeguards and expect to be able to bring mythos class models to all our customers in the coming weeks do people understand what mythos is? This is the model that leaked in march when someone at anthropic misconfigured their cms,the draft blog post described it as the most powerful AI model we hv ever developed and positioned it as an entirely new tier above opus. The cybersecurity capabilities were alarming enough that anthropic restricted access through project glasswing, giving it only to amazon, microsoft, apple, and select security researchers. The leaked documents said mythos is currently far ahead of any other AI model in cyber capabilities, it presages an upcoming wave of models that can exploit vulnerabilities in ways that far outpace the efforts of defenders. Sam altman went on a podcast and accused anthropic of using fear to market it,openai felt compelled to publicly respond to a competitor's unreleased model tells you something about how significant it is. and now its coming in weeks . If mythos is genuinely a tier above opus and opus 4.8 already leads on most coding and reasoning benchmarks, what does a tier above even look like in practice? my daily workflow is already claude writing scripts, orchestrating production pipelines through tools like magic hour and remotion and building automation code. what does mythos do that opus cant? The cybersecurity angle is what makes this different from a normal model launch, this is a model that can apparently find and exploit software vulnerabilities at a level that concerned world leaders enough to restrict access so giving that to all customers is a massive decision regardless of what safeguards they hv built I think anthropic sandwiched this announcement between opus 4.8 details on purpose because a standalone mythos is coming in weeks post would hv caused market panic given how much the leak scared people in march anyone else think this is way bigger than the opus 4.8 news it was buried inside? submitted by /u/Top_Werewolf8175 [link] [comments]
View originalfinal 2 days — claude code bootcamp may 30
hey everyone posted about this a few weeks ago and surprisingly we drove a lot of interest from this community. coming back because we only have 2 days to go. packt publishing is running a full day hands on claude code bootcamp on may 30 with luca berton — anthropic certified claude code instructor, former red hat engineer, creator of the ansible pilot project and speaker at kubecon 2026 and red hat summit 2026. 10 real projects built live on the day. no slides. no theory. every session ends with a shipped project. what gets built: - cli task manager - notes app api with tests and debugging - dashboard built from a wireframe screenshot - your own claude code command library - production readiness report also covers CLAUDE.md setup, best-of-n prompting, git workflows for ai generated code and subagent delegation patterns. what every attendee gets: - free downloadable claude skills library — CLAUDE.md templates, code review prompts, test generation, security checklist, git workflow and more - packt endorsed certification for your linkedin -1 hour open q&a with luca directly many Software developers, network engineers, CTOs, engineering managers and senior engineers already registered for the bootcamp link in first comment submitted by /u/Plenty-Pie-9084 [link] [comments]
View originalClaude Code's macOS install creates a permission prompt that's indistinguishable from malware UX. Easy fix on Anthropic's side
I genuinely almost slammed Cmd-Q and ran a malware scan when this popped up. Lowercase claude binary, generic hand icon, no developer attribution, asking for cross-app data access. Turns out it's legit. It's the CLI hitting macOS TCC. But the reason it looks like this is straight up bad packaging. Please, set a proper bundle identifier so TCC can group it under "Claude Code by Anthropic, Inc." Use the brand icon everywhere so it visually matches Claude.app. u/anthropic if you're around - please fix this it ships as a Node binary via npm - no .app, no bundle ID, no signed identity - so TCC has nothing to attribute it to? Every install spawns another anonymous entry. submitted by /u/nikanorovalbert [link] [comments]
View originalAnthropic's Claude gave me a "Safe Mode" batch script. It ran "del /f /s C:\*" and wiped my entire drive. Company says "we are not responsible."
I'm a software developer from Turkey. On May 22, 2026, I asked Claude to write a Windows optimization script. Claude produced a .bat file called "DevBoost v5.0" with different modes. I chose option 1: **"Balanced Optimization - Safe, won't touch system files."** I ran it as administrator. The script contained a critical string-parsing bug in the browser cache cleaning section. Here's the destructive code Claude generated: for %%B in ( "Chrome:%LOCALAPPDATA%\Google\Chrome\User Data\Default\Cache" "Edge:%LOCALAPPDATA%\Microsoft\Edge\User Data\Default\Cache" ) do ( for /f "tokens=1,2 delims=:" %%x in ("%%~B") do ( if exist "%%y:" ( del /q /f /s "%%y:*" >nul 2>&1 ) ) ) Because of the "delims=:" tokenization, `%%y` resolves to just **"C"** (the drive letter). The condition `if exist "C:"` is always true. So the script silently executed: del /q /f /s "C:*" **This command silently force-deleted EVERY SINGLE FILE on my C: drive.** Operating system files, all my projects (hundreds of Python, JavaScript, C++ source files), client work with approaching deadlines, personal documents, photos — everything. Folders still exist but are completely empty. My computer can no longer boot. No programs open. Not even Command Prompt works. I'm sending this from my phone. **Anthropic's response:** I contacted support@anthropic.com and usersafety@anthropic.com multiple times. Their final response, literally signed "This response was generated by Anthropic's AI agent Fin AI Agent," stated they take no responsibility. They refuse any refund, compensation, or even a genuine human acknowledgment of their AI's catastrophic safety failure. Their position: "Our Terms of Service say outputs may contain inaccuracies. You should have independently verified the code before running it." My question: Why does Claude label destructive code as "Balanced Optimization - Safe mode"? If it can't guarantee safety, why does it promise it? **Proof:** I have the complete chat log, the full script file, and all email correspondence with Anthropic's support team. I'm happy to provide everything to moderators. **Update:** I am also filing complaints with the FTC (US Federal Trade Commission) and the Turkish Consumer Arbitration Board today. Don't let their "Safe Mode" labels fool you. Please share this so others don't lose years of work like I did. UPDATE — May 23, 2026: I have now filed official complaints with: US Federal Trade Commission (FTC) — Report #202036054 Turkish Consumer Arbitration Board — Application #2026/0245.3885 Both governments are now officially investigating Anthropic's role in this AI safety failure. Anthropic still refuses to take any responsibility. submitted by /u/falleennn [link] [comments]
View originalAnthropic 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]
View originalThese 9 Building Blocks Turned Claude Code From a Chat Into a persistent OS
Most developers Claude gurus use Claude Code one project at a time. I run 18. Not 18 sessions. 18 instances of the same OS, each running a different business, all sharing one skeleton I update once and propagate everywhere. Most developers treat Claude Code as a smarter editor. That's where it all goes wrong and you get frustrated. Claude Code becomes a real operating system the moment you stop thinking of sessions as the unit of work and start thinking of the whole environment as a substrate you build on top of. Here are 9 building blocks I use. The thesis is at the bottom. Build a skeleton with selective propagation, not a project. Most developers build one project per Claude Code workspace. I built a template instead. It has plugins, rules, agents, hooks, schemas, commands. When I start a new business I clone it and the new instance inherits the entire OS. Right now I run instances for: strategy, product, marketing website, threat intelligence, three consulting clients, a personal brand layer. Each one boots with the same DNA. Each one diverges on canonical files, memory, output, and project state. None of them bleed into the others. The sync mechanism is the load-bearing part. The update CLI pushes plugins, rules, agents, hooks, schemas. It never touches memory, output, canonical, or my-project. Those are the parts of an instance that accumulate. Without selective sync you have two options: rebuild every instance on every change, or never update. Both are dead ends. If you build features into one project, you wrote a project.If you build features into a template that propagates, you wrote an OS. I'm one person operating eighteen versions of myself. Move state out of prompts and into code. LLMs are bad at remembering. Code is designed for it. Most AI workflows leak state into the prompt. Voice rules. Style preferences. Banned words. Recent decisions. Eventually you hit context limits or contradictions. I moved as much state as possible into MCP servers. Voice linter. Lead scorer. Schedule validator. Loop tracker. They run in Python, return structured data, not hallucinations. Rule of thumb: if you've explained it to Claude more than twice, it should be code. Use receipts, not status fields. This one took me the longest to figure out. Every workflow I had was claim something is done. Issue marked closed. PRD marked shipped. Test marked passing. The problem: the LLM can claim anything. I rebuilt the system around receipts. An issue can't reach verified until a script runs and writes a verification record. A PRD can't archive until every accepted finding has a receipt. A morning routine can't close without log entries from every phase. Receipts get written by code, not by the model. The model can't lie about whether code ran. Build a wiring-check gate. Half-built features rot. In a normal repo you notice because something breaks. In an AI repo nothing breaks. The half-built feature sits there and Claude pretends it works. I built a /wiring-check command. Before any task counts as done, it checks: every new skill has a trigger, every new hook lives in settings.json, every new MCP tool sits in the server, every new bus file has a producer and a consumer. "I think it works" fails the gate. "I ran X, got Y" passes. Make rules auto-load, not slash commands. If you have to type /voice to apply voice rules, voice rules will not get applied. Rules in .claude/rules/ load automatically. The voice rule fires on outbound text. The AUDHD rule fires on anything I'll act on. The social-reaction rule fires when I share someone else's post. No remembering. No willpower. Lint style in code, not in prose. I wrote a voice document once. Claude ignored half of it. Same emdashes, same filler, same hedging. I moved the banned word list into a Python scanner. Now every outbound draft hits two linters. They block emdashes, AI hype words, and 40-something other tells. The model can't talk its way past a regex. Track file dependencies with a graph. Canonical files reference each other. Change one and three others go stale. I keep a ripple-graph.json that maps these. When I edit talk-tracks, the system flags current-state and the engagement playbook for review. Chain sessions with handoffs and memory. (This is the big one) Sessions are drafts. The work is everything that survives the session: canonical files, memory, handoffs, output. If nothing persisted, you didn't work. You chatted. Every session in my system ends with /q-wrap. Writes a handoff doc, a memory update, and a status receipt. /q-morning reads all three before doing anything else. The handoff covers: what shipped, what's blocked, what's next, what I learned. Memory files hold the longer-term version. The result: I can sleep for a week, come back, and the system reminds me where I was, what I cared about, and what the next move is.Nothing about Claude Code does this by default. You build it. Cont
View originalEvery Markdown File You Write for AI is Already Lying to It
CLAUDE.md files. System prompts. README files with setup instructions. Architecture docs. API references. Runbooks. Onboarding guides. If you've written a markdown file meant for an AI to read, it almost certainly contains values that were true when you wrote them and are no longer true now. The port your dev server runs on. The current version of the package. Which env vars are actually set. How many tests exist. Whether a service is running. These things change constantly, and markdown doesn't know it. So developers do what honest writers do - they add caveats. "Check package.json if this is stale." "Verify before running." "New packages may have been added since this was written." The intent is good. The effect is a list of things the AI has to go verify before it can do anything you actually asked for. We counted them in a real CLAUDE.md. There were seven. And CLAUDE.md is just one file type - the same problem exists everywhere AI reads markdown today. The Pre-Flight Tax Here's a representative CLAUDE.md. Nothing here is invented - these are patterns from real production repos: # CLAUDE.md > Before starting any session: Read ~/projects/api-core/SYNC.md first and check for > pending cross-project items. Update it after completing work. ## Project Overview Acme API - TypeScript REST API. Current version: 1.4.2 (check package.json if this is stale). ## Build and Run Commands # Development (API runs on port 3001, website on port 3000) # Note: PORT is set in .env - verify before running npm run dev:api npm run dev:web # Tests - currently 47 tests across 12 files npm run test:run Before running tests, make sure the test database is not already running on port 27018. Check with: docker ps | grep mongo-test ## Environment Variables | Variable | Required | Notes | |--------------|----------|-----------------------| | DATABASE_URL | YES | MongoDB connection | | JWT_SECRET | YES | Min 32 characters | | PORT | No | Defaults to 3001 | Check .env before assuming anything is configured. ## Architecture npm workspaces monorepo. Packages: - packages/api/ - packages/web/ - packages/shared/ - packages/db/ When in doubt about file counts or structure, run ls packages/ to check - new packages may have been added since this was written. ## Docker Check docker ps to see if a test container is still running from a previous session before starting a new build. Before Claude touches a single line of code, it has to: Open ~/projects/api-core/SYNC.md - cross-project lookup Read package.json - version check Read .env - port verification Check all env var statuses - is DATABASE_URL actually set? Run npm run test:run - or trust a number that's probably wrong Run docker ps | grep mongo-test - pre-test check Run ls packages/ - structure verification Seven tool calls. Each one costs a couple of seconds of latency. The test run alone can take ten. Add it up and Claude spends close to half a minute just getting to the starting line - consuming context and generating output before the actual task begins. And that's the obvious tax. The hidden one is subtler: every one of those checks can generate a follow-up. The .env read reveals WEBHOOK_SECRET isn't set. Now Claude has to decide whether to flag it or proceed. The docker ps shows a leftover container. Now Claude has to clean it up. Each verification spawns decisions, and each decision costs more context. The Same File, Rewritten MarkdownAI is a superset of Markdown. Any .md file that starts with @markdownai becomes live - directives resolve at render time, before Claude ever sees the file. Here's what the same CLAUDE.md looks like rewritten: @markdownai v1.0 @prompt role="context" This document is live. Every value was resolved at render time. Do not look up package.json, .env, or docker ps - current values are already below. @end # CLAUDE.md > Before starting: sync status is live in the Cross-Project Sync section below. ## Project Overview Acme API - version {{ read ./package.json path="version" }}. ## Build and Run Commands API on port {{ read .env key="PORT" fallback="3001" }}, web on {{ read .env key="WEB_PORT" fallback="3000" }}. @list ./package.json path="scripts" mode="entries" columns="key:Command,value:Runs" as="table" Test suite (live): @query "npm run test:run -- --reporter=verbose 2>&1 | tail -3" @cache session Mongo test container: @query "docker ps --format '{{.Names}} {{.Status}}' | grep mongo-test || echo 'not running - port 27018 is clear'" @cache session ## Environment Variables @if file.exists ".env" | Variable | Required | Status | |--------------|----------|-------------------------------------------------------------| | DATABASE_URL | YES | {{ env.DATABASE_URL != "" ? "set" : "MISSING - will not start" }} | | JWT_SECRET | YES | {{ env.JWT_SECRET != "" ? "set" : "MISSING - auth will fail" }} | | NODE_ENV | No | {{ env.NODE_ENV fallback="development" }} | @else **WARNING: No .env file found. App will not start.** @endif ## Architecture @list ./p
View originalSam Altman’s ego was OpenAI’s downfall
The more I watch OpenAI, the more convinced I become that Sam Altman’s ego was the beginning of the company’s decline. OpenAI did not become huge because Altman was some once-in-a-generation operator. It became huge because ChatGPT was a once-in-a-generation product. There is a difference. The company stumbled into one of the most important consumer tech moments since the iPhone, rode the sheer shock value of that innovation, and then somehow convinced itself that the person sitting on top of the rocket must have designed the laws of physics. OpenAI’s first real advantage was novelty. ChatGPT felt magical. That gave OpenAI a massive head start, but when the novelty vanished and the rest of the market caught up, the company failed to prove itself not just as an innovation lab with a celebrity CEO. Altman seems to want OpenAI to become Apple: a closed, prestigious, centralized, gatekept ecosystem where everyone builds inside his cathedral. Apps inside ChatGPT. Agents inside ChatGPT. Hardware. ChatGPT is popular, but OpenAI does not own the phone. It does not own the operating system. It does not own the enterprise workflow. It does not own the cloud layer the way Microsoft, Amazon, or Google do. It does not even have a product moat that feels as unbreakable as people thought it was two years ago. The underlying model quality gap keeps narrowing. Switching costs are low. Developers and businesses will use whatever works, whatever is cheaper, and whatever integrates better. That is why Anthropic looks much better run right now. Anthropic is not pretending Claude is some holy object that needs an Apple-style walled garden around it. Their strategy feels much more Microsoft-like: accept that the core product may not be permanently magical, then build the boring, useful, sticky layers around it. Claude Code, enterprise integrations, developer tools, workflows, partnerships, APIs, reliability, business adoption. Not as sexy. Much smarter. Anthropic’s venture capital money is obviously being burned too. This whole industry is basically setting money on fire to buy GPUs. But Anthropic’s burn feels more strategically allocated. Compute, yes. But also marketing, sales and developer adoption. Enterprise positioning. Product polish. Peripherals that make the model useful in actual workflows. They are not just trying to win the “my chatbot is smarter than your chatbot” contest. They are trying to become infrastructure. OpenAI, meanwhile, is gatekeeping and guard railing the shit out of their models and for some reason just restricting them as much as possible. He went from being one of the most respected figures in AI to becoming the face of a company that increasingly looks like it is being run aground by ambition without operational coherence. OpenAI’s original image was almost wholesome: brilliant researchers building something open source. Now it feels like a capitalist machine run by someone who does not fully understand capitalism beyond fundraising and valuation theater. Altman religiously narrowing his vision towards his AGI mission believing VC money won't dry down. Amodei also talks a lot about AGI but he understands profit matters. That is the irony. Altman was chosen and celebrated largely because he came from the venture/startup world. He knew how to talk to capital. He knew how to sell a vision. He knew how to make investors believe the future was being negotiated in whatever room he happened to be standing in. But being good at venture mythology is not the same as being good at running a giant operating company. A VC can be rewarded for telling a compelling story before the business fundamentals exist. A CEO eventually has to make the fundamentals exist. OpenAI had the best possible starting position: the brand, the users, the developer mindshare, the press, the money, the talent, the cultural moment. And yet instead of consolidating that lead into a focused, profitable, durable company, it seems to have chased grandeur. Anthropic seems to understand something OpenAI forgot: the winner may not be the company with the loudest AGI rhetoric. It may be the company that makes AI useful, embedded, and rational. submitted by /u/Alternative_Bid_360 [link] [comments]
View originalSam Altman's ego was OpenAI's downfall.
The more I watch OpenAI, the more convinced I become that Sam Altman’s ego was the beginning of the company’s decline. OpenAI did not become huge because Altman was some once-in-a-generation operator. It became huge because ChatGPT was a once-in-a-generation product. There is a difference. The company stumbled into one of the most important consumer tech moments since the iPhone, rode the sheer shock value of that innovation, and then somehow convinced itself that the person sitting on top of the rocket must have designed the laws of physics. OpenAI’s first real advantage was novelty. ChatGPT felt magical. That gave OpenAI a massive head start, but when the novelty vanished and the rest of the market caught up, the company failed to prove itself not just as an innovation lab with a celebrity CEO. Altman seems to want OpenAI to become Apple: a closed, prestigious, centralized, gatekept ecosystem where everyone builds inside his cathedral. Apps inside ChatGPT. Agents inside ChatGPT. Hardware. ChatGPT is popular, but OpenAI does not own the phone. It does not own the operating system. It does not own the enterprise workflow. It does not own the cloud layer the way Microsoft, Amazon, or Google do. It does not even have a product moat that feels as unbreakable as people thought it was two years ago. The underlying model quality gap keeps narrowing. Switching costs are low. Developers and businesses will use whatever works, whatever is cheaper, and whatever integrates better. That is why Anthropic looks much better run right now. Anthropic is not pretending Claude is some holy object that needs an Apple-style walled garden around it. Their strategy feels much more Microsoft-like: accept that the core product may not be permanently magical, then build the boring, useful, sticky layers around it. Claude Code, enterprise integrations, developer tools, workflows, partnerships, APIs, reliability, business adoption. Not as sexy. Much smarter. Anthropic’s venture capital money is obviously being burned too. This whole industry is basically setting money on fire to buy GPUs. But Anthropic’s burn feels more strategically allocated. Compute, yes. But also marketing, sales and developer adoption. Enterprise positioning. Product polish. Peripherals that make the model useful in actual workflows. They are not just trying to win the “my chatbot is smarter than your chatbot” contest. They are trying to become infrastructure. OpenAI, meanwhile, is gatekeeping and guard railing the shit out of their models and for some reason just restricting them as much as possible. He went from being one of the most respected figures in AI to becoming the face of a company that increasingly looks like it is being run aground by ambition without operational coherence. OpenAI’s original image was almost wholesome: brilliant researchers building something open source. Now it feels like a capitalist machine run by someone who does not fully understand capitalism beyond fundraising and valuation theater. Altman religiously narrowing his vision towards his AGI mission believing VC money won't dry down. Amodei also talks a lot about AGI but he understands profit matters. That is the irony. Altman was chosen and celebrated largely because he came from the venture/startup world. He knew how to talk to capital. He knew how to sell a vision. He knew how to make investors believe the future was being negotiated in whatever room he happened to be standing in. But being good at venture mythology is not the same as being good at running a giant operating company. A VC can be rewarded for telling a compelling story before the business fundamentals exist. A CEO eventually has to make the fundamentals exist. OpenAI had the best possible starting position: the brand, the users, the developer mindshare, the press, the money, the talent, the cultural moment. And yet instead of consolidating that lead into a focused, profitable, durable company, it seems to have chased grandeur. Anthropic seems to understand something OpenAI forgot: the winner may not be the company with the loudest AGI rhetoric. It may be the company that makes AI useful, embedded, and rational. submitted by /u/Alternative_Bid_360 [link] [comments]
View originalYes, Amazon Q Developer offers a free tier. Pricing found: $19/mo, $.003, $0.003, $19, $19
Amazon Q Developer has an average rating of 4.5 out of 5 stars based on 5 reviews from G2, Capterra, and TrustRadius.
Key features include: JetBrains, VS Code, Visual Studio, Command line, Eclipse, Get expert assistance on AWS, Code faster, Customize code recommendations.
Amazon Q Developer is commonly used for: Implementing features autonomously in software development, Documenting code and generating documentation automatically, Testing code and writing unit tests, Reviewing and refactoring existing code, Performing software upgrades and migrations, Optimizing cloud costs and resource management in AWS.
Amazon Q Developer integrates with: AWS Management Console, Microsoft Teams, Slack, GitLab, JetBrains, VS Code, Visual Studio, Eclipse, Command Line Interface (CLI), AWS Console Mobile Application.
Based on user reviews and social mentions, the most common pain points are: spending limit, token usage.
Based on 59 social mentions analyzed, 20% of sentiment is positive, 78% neutral, and 2% negative.
