Copy.ai

Copy.ai AI

Copy.ai AI

Copy.ai AI

Copy.ai AI

Copy.ai AI

Copy.ai AI

Copy.ai AI

Copy.ai AI

Copy.ai AI

Copy.ai AI

We're turning into prompt managers, not craftsmen. Anyone else seeing this?

Look around. Every other product launching right now is some variation of "AI-Powered [insert buzzword]." They're everywhere. Modern tools have given founders and developers a convincing illusion of omnipotence: idea hits, feed it to an LLM, stack some agents on top, and MVP is done in a weekend. https://preview.redd.it/37ocn6azkv1h1.png?width=1672&format=png&auto=webp&s=06d4a9ef986d56a9eb3417e67a3524c18e73e100 Sounds great, right? On the surface, yes. But underneath that fast-launch facade, something is quietly rotting: thinking is getting commoditized, and we're losing craft. Real mastery in any field takes years of practice, failure, and deep focus. Today, apparently everyone is a master for $20 a month. That's a lie we're telling ourselves. Just look at how much panic a 5-hour rate limit window in Claude generates online. Tokens run out, and suddenly people have two options: wait for the reset like a metered parking spot, or upgrade. It's like a Michelin-starred chef who can no longer taste food, just dictating to a chatbot: "make me a pasta." Without the subscription, he can't cook. The counterargument: "But orchestrating AI IS the new skill." Fair. But it's a horizontal skill, not a vertical one. You learn to coordinate agents while losing deep domain knowledge. Think conductor versus virtuoso violinist. A conductor is impressive - but if the orchestra walks off stage, can he play a solo that makes the room go quiet? This is most visible in developers right now. People who got used to copy-pasting from Cursor or Claude hit a wall on hard architectural problems. When a product grows, starts needing real trade-offs, starts buckling under load - prompts stop working. The muscle for hard problems atrophied because they never had to build it. Same thing is happening to analysts, marketers, designers, researchers. My position: barbell, not crutch Running out of tokens doesn't scare me. My foundation means I can work regardless of what's left in my quota, whether there's internet, whether a subscription is active. The only thing that throws me off is running out of good coffee. I use LLMs heavily. But with one condition: AI is a barbell, not a crutch. It sharpens my own work - it doesn't replace the parts I care about. The fastest, most tireless junior I've ever hired. But the senior judgment and the final call always stay with me. Two types of professionals The market is already splitting into two groups. Token-dependent: live limit to limit, panic when Anthropic or OpenAI have an outage, can't produce anything original without a prompt to lean on. Token-independent: use AI as a force multiplier but can, at any moment, sit down and do the work themselves - with more depth, more precision, better judgment. The second group will command much higher rates. When the world is drowning in mediocre AI-powered software and content - and it will be - clients and employers will pay serious money for people who actually understand what they're building and why. Curious whether others are feeling this shift. Are you building toward token-independence, or does the dependency not bother you? submitted by /u/digdiver [link] [comments]

Solo indie game developer, new grad no formal SWE experience in love with how productive Claude has made me

My game has gone through a few iterations at this point, but Claude, specifically Claude Code has been game changing for me. Started in the desktop app with 3.5 haiku, now on the max plan with Claude Code. I'm interested to hear from other recent college grads that have built something with these new coding tools. I don't know how much of my project I should attribute to Claude Code, my education, my sheer persistence, or all of the above. Not saying my game is bullet proof BY ANY MEANS, but it's WAY more than I would've ever been able to build without CC. Basically 100% of the code has been written with Claude Code, or copying and pasting over from Claude's desktop app before Claude Code was a thing. Some highlights of what Claude helped me out with: - No wasting time reading syntax docs for libraries, understand what libraries function is -> implement - Real-time multiplayer up to 10 players per lobby - Cost-optimized serverless GPU autoscaling (minimizing GPU costs) - Mobile first phone as controller UX like Jackbox, or Kahoot -Mobile browser socket connection troubleshooting -R2 bucket policy deletes prompts and images daily -Open source image model, presented cold start challenges 6 months ago I was a new grad with no SWE experience. Today I'm running https://imageclash.net. It's real-time multiplayer party game focused on creative, comedic, AI image generation in a competitive format (think Cards against humanity with AI images). Players create prompts → AI generates images → everyone votes on the funniest ones. Just wanted to share because Claude Code is genuinely incredible for solo builders with limited experience. This project would have been impossible for me on my own, and it has always been my dream to build games submitted by /u/Dsc_004 [link] [comments]

The Borrowed Hour: A two-tier LLM adventure engine

Tl;dr: Created an LLM text adventure engine called The Borrowed Hour inside a Claude Artifact. It uses a two-tier model handoff (Sonnet for openings, Haiku for gameplay) and a forced state machine to keep the AI from losing the plot. It features a unique post-game "Author’s Table" where you can debrief with the AI. P.S. The Claude Artifact preview environment handles API calls differently than the published environment. Prompt caching was removed because it broke the published Artifact. The game View on GitHub (MIT licensed) (Repo made with Claude Code) Play a demo (Claude Artifact) This is another LLM text adventure. I know these have existed for years, but the key difference is that it's architecture is de novo (i.e. built without prior knowledge because I never intended to build this and therefore skipped the part where I looked at the SotA/prior art). How it started It started simple: I just wanted to play a quick game, so I asked Haiku to play GM for a text adventure, but with more freedom than just typing "open door" or "inspect gazebo" (iykyk). Haiku instead built an entire UI inside the chat and things escalated from there. I used Claude's chat interface instead of Claude code like a caveman banging rocks together. I'd feed it ideas, but Claude was the architect and would push back. The starting prompt was just "Create a text-based adventure that allows for more freedom than just 2-word answers." Then I just kept playing and returning information on what I wasn't satisfied with. The narration was too long, the model kept losing the plot. I added ideas for 3 out of 4 pre-built narratives (a subtle time loop, climbing a cyberpunk syndicate ladder, a vision of the future that needs to be prevented, and one that Claude designed freely) and I ensured that the story actually ends once objectives are met instead of just wandering off into aimless chatting. The final artifact that was built is The Borrowed Hour. You'll recognize the typical Claude design language pretty easily. Game mechanics Before getting into the design/architecture, it helps to know how the game works. There are no dice rolls / stats / perception checks. Success relies on your ability to draft a narrative that fits the lore. If you play it smart, you are effectively the co-GM. You can type anything you want from single words to elaborate plans and lies. If your invention sounds plausible, the GM usually rolls with it. In one run, I needed to get an NPC into a restricted temple. I invented a fake piece of temple doctrine about sanctuary. Because it fits the world's internal logic, Haiku just accepted it and made it canon. In order to help keep track there's a ledger that updates each turn to show what your character knows: inventory, NPCs, clues, and a rolling summary. Designing the architecture This was challenging, but it's the fun part for me. The model is forced through a structured tool call on every turn. This was the key to making the game stable, but as the P.S. explains, getting this to work reliably in the published environment required abandoning another key feature (prompt caching). Sonnet writes the opening scene because that first page sets the tone and voice for the rest. Then Haiku takes over for all the continuation turns. This keeps the cost down drastically without ruining the style, because Haiku can imitate Sonnet's established prose. I initially used a binary good/bad ending system, but it forced complex emotional stuff into the wrong buckets. Now there are five ending states: good, bittersweet, pyrrhic, ambiguous, and bad. Helping a dying woman find peace in the Dream scenario isn't a good ending, it's bittersweet. The model is instructed to commit to one of these and officially close the game when the target is reached. One thing that was added were player-initiated endings. If you type "I give up", even on the very first turn, the GM is now explicitly instructed to close the narration and set ending: bad. The author's table is probably the most interesting feature for a text adventure. Once the game ends, the Artifact can switch into a meta mode. In this mode you can ask what plot points you missed, which NPCs mattered, what alternative branches existed. The GM is prompted to admit mistakes instead of inventing defenses if you point out a plot hole. This mode exists because I wanted to argue about plot holes and narrative inconsistencies (lol). Quirks, bugs, and lessons learned The design works well overall, but it's not bulletproof. LLMs can't keep secrets Keeping things secret is incredibly difficult for an LLM. There's two main hypotheses: Opus calls it inferential compression, (which is deducing fact C on the players behalf based on evidence A and B, e.g. when the player sees Lady Ardrel say she saw a copper ring on Lord Threll, and the player previously had a vision of an assassin wearing such a ring, the ledger should not say Threll is the assassin. It should say Ardrel

I think I got a "few" features in before Anthropic just adds them next week

Managing multiple terminal windows is a nightmare in terms of keeping track of which terminal screen is which, having to check if any particular terminal has completed its process activity, compilation or AI coding agent activity, as well as having your mac dock full of little black box icons contributing to the messiness. This time-sink and annoyance is compounded by the existing limitations of the standard terminal screen, which is long overdue for an update. Terminal Conductor has been built around three core goals: · Eliminate the friction of having multiple terminal windows open in your workspace. · Incorporate useful and practical functionality to terminal for working with AI coding agents. · Modernize terminal with features that should have been added 30 years ago. Once you use the new Terminal Conductor app, you’ll never have a reason to open the regular version of terminal again. www.terminalconductor.com and on the mac app store at https://apps.apple.com/us/app/terminal-conductor-ssh/id6762624479?mt=12 . Goal Number 1: Make managing dozens of terminal windows feel harmonious To achieve this, all of the terminal windows are launched within a single app as separate tabs, similar to an internet browser. · Each tab can be renamed, and will change color if you have a coding agent running inside it. A blue gradient on the tab indicates Codex is running, orange for Claude Code, and so on. I mean what other color but orange would I have given for Claude? · Also on par with the modern browser theme is Vertical Tabs mode, so you can cycle through your list of named tabs to instantly switch from one terminal screen to another. · You can arrange your tabs in groups, making it easy to track which screens are part of which project. You can even hide a group’s tabs for further control of your workspace. · Bulletproof sessions. Every new tab is a shell (and tmux for SSH). This means that if the app closes, or you lose your SSH connection, or any other disconnect happens like your laptop sleeps, you can just relaunch. Every tab is right where you left off, with running agents still running. Lose your anxiety, not your work. · Split panel option to view several terminal windows at the same time. · Customize each terminal window with their own visual themes and borders. · Each tab has a dot that pulses big-to-small when active processes are occurring (ie, if a coding agent is thinking or typing a response). The dot remains static if the terminal window is idle or the agent is waiting. · Every terminal screen (that you didn’t directly terminate) that gets detached can be called up again from the favorites drop-down menu. And you can pin your favorite go-to environments (docker containers, SSH addresses) to seamlessly connect in a new tab. · Optional local password retention in case you want to instantly connect to an SSH and skip manually entering your password each time you connect. · Rest easy: No data telemetry, no analytics, no data transfers. Absolute privacy. Goal Number 2: Add practical AI agent functionality to terminal · From the Groups screen, you can broadcast text that will immediately go into the command line of all terminals within that group. For instance if you have 5 tabs in “Group A” all running Claude Code, you can broadcast “/compact” and all 5 will now compact. Or “Update memory.md and changelog.md”, and so on. · You can also instruct an agent in one tab to message the coding agent in another tab with u/tab and u/group commands. · For each of the CLI coding agents (Claude, Codex, Gemini, Kimi and Qwen), entering a /fork command immediately creates a second tab with the branched conversation in there. This is how forking should be. And forked terminal tabs are automatically assigned to the same group as their parent terminal tab. Additionally, Terminal Conductor adds forking to Gemini CLI and Qwen CLI since they don’t have it natively. · One-click conversation resume. When you exit a conversation and it gives a resume command (ie, “codex resume 345332-23423-29875-34223” or “claude -r 23453-34556-3432-234256”, you can highlight that resume command and save it to your Saved Conversation list displayed in the collapsable right panel. The save action includes a little blurb space that lets you say what the convo is about, and a button to affix the bypass permissions, --dangerously-skip-permissions, -yolo suffix. In the options menu you can even toggle on/off automatically answering the “Do you trust this folder” question when starting up the agent. Breeze right into your work. · Speaking of the right panel (which can be maximized/minimized), you get agent-aware info such as your custom commands (when you make your own forward slash commands) and the official commands. Terminal Conductor automatically detects which coding agent you’re using and calls up the list of their specific official forward-slash commands. The right panel also lists your custom agents you saved. · Th

Claude code reviewed by another AI

So I have been using Claude.ai for a while now (not claude code), and impressed, probably one of the best ai I have used, but I do know that some ai have streangths and weaknesses for code production, I have also started using other ai to review the code, and they have been successful in finding bugs, improvements or even suggestion a better approach, is this something others do ? One thing, just copying the code to another ai will give a different result than copying the code and giving it full context from the prime ai wherte the prime ai can write a few context documents how do others handle this if you do it at all ?, is copying the code only enough ? cheers submitted by /u/Electrical_Chard3255 [link] [comments]

Accidentally built something useful while trying to fix my own terrible prompting

I wanted to fix my own problem that I'm consistently running into with AI so I built a tool to fix it. I use AI constantly but kept getting mediocre outputs because my prompts were lazy and vague. Every "optimized prompt" I found online was just a template full of brackets and placeholders I still had to fill in myself. My brain just registers this as more work than typing something bad in the first place. So I vibe-coded a tool with Claude to fix it. You type whatever you're thinking, pick a category, and it generates 6-10 fully written prompt variations. No brackets, no blanks, nothing to fill in. Recently added two things I've found genuinely useful: A "Try it" button on each prompt that opens Claude, ChatGPT, or Gemini with the prompt already loaded (to cut out the additional step of copying and going over to your model to paste). And a scoring feature that rates each variation out of 100 with a one-line breakdown of what makes it work or where it falls short (to help you decide which prompt you want to run with). Example: (Ran for - Model: Claude, Category: Writing, Variations: 6 prompts, Complexity: Simple) Input: "help me write a cover letter" Output: I'm writing a cover letter and need it to be laser-focused. Constraints: no more than 250 words total, zero clichés (no 'passionate' or 'team player'), every sentence must directly address something from the job posting, and the tone should be professional but conversational. Help me draft it with these guardrails in mind. https://www.promptimize.app to try. Feedback is highly encouraged bad or good. Thank you. submitted by /u/Less-Mud5677 [link] [comments]

How to get a job in ai

Pretty much what the title says. I'm asking for my nephew. Who's a copy writer? And those jobs are going to AI. So I'm thinking, maybe he should learn or get certified in AI. Is there a school or a program or something? That's legit that would get him a certificate that would help him. The employment in the AI world submitted by /u/stilllearning70 [link] [comments]

We built a process layer on top of Claude Code that handles context and coordination across tasks

Over the past year, we have been using a variety of AI coding tools across different project teams, including Claude Code. We saw that the individual productivity went up but those gains didn't compound across the teams as much as we were hoping for. We figured that the reason was that much of the process around coding was still largely the same, all the way from sprint planning to standups to PR reviews (with some AI sprinkled). The losses were particularly stark at handoff points. Context gets lost at each handoff and has to be reconstructed over and over again. It starts to show a copy of a copy effect, causing quiet drift and maintenance issues that erode the initial productivity gains. So we built a layer on top that handles context and coordination across tasks. Each step in the engineering process declares what it reads and what it produces. The architecture review consumes the spec, produces an ADR and module guidance. The dev task receives that ADR plus the pitfalls file for the modules it touches. The reviewer gets the spec, the ADR, and the diff. Each session gets dispatched with exactly the right context loaded. This allows the project's context to grow over time, and for the right pieces of the context to be made available to the right tasks, without requiring the engineers to work harder and harder to make that happen. This in turn has allowed us to rely on this process layer for better quality code as opposed to the individual discipline of engineers. We do still use Claude Code directly for simpler tasks since the overhead math on smaller spikes is different. Anyone else thinking about this as a process/coordination problem rather than a tools problem? submitted by /u/ttariq1802 [link] [comments]

Replaced my $15/mo Wispr Flow subscription with a free local macOS app I built using Claude Code

I spend most of my day writing prompts to Claude. Read a study recently that said people speak ~3x faster than they type, which lands differently when "writing" is basically your whole workflow. Looked at Wispr Flow – it's genuinely great, but $15/month forever for something I'd mostly use to dictate to Claude felt wrong. So I spent two weeks of evenings building my own with Claude Code. How Claude helped I'd never shipped a Tauri / macOS app before this. Claude Code did the bulk of the actual code: The menu bar app structure, global hotkey capture, and paste-anywhere flow UI and onboarding Integrating the local model runtimes (Parakeet / Whisper for transcription, Gemma 4 for polishing) The model download / storage logic so the app ships without bundling gigabytes of weights A lot of debugging I would not have had the patience for on my own I made the product and design calls; Claude wrote the vast majority of the code. Two weeks of evenings, usually an hour or two at a time. What it does Menu bar app for macOS. Hold a hotkey, talk, release – text is copied to your clipboard. Works in any app: Claude.ai, Cursor, Slack, browser, IDE, whatever. Two open-source models doing the work: Parakeet (NVIDIA) / Whisper for transcription Gemma 4 (Google) / Apple Intelligence for polishing the raw transcript into something readable Everything runs locally. No cloud calls, no API keys, no telemetry, no account. Fully offline after download. Free for personal use, no signup. Download: https://vox.rizenhq.com/ Caveats macOS only. Apple Silicon required (M-series chip). Windows build is next. It's two weeks old. Bugs I haven't found yet exist. ~90% of Wispr Flow's quality, not 100%. Enough for me to use every day. What it's saving me 40–60 minutes a day, mostly on prompts. Dictating to Claude feels noticeably more natural than typing to it. The ask Feedback, especially from people who talk to Claude a lot: Where does it break? Bug reports > compliments. What did you use it with? What feature would make you switch from Wispr Flow (or start using voice-to-text at all)? Tech notes No separate model download – onboarding handles it Gemma 4 options: E2B, E4B, 26B. E2B runs on phones; 26B is overkill for most machines. I use E4B – great quality, fast. RAM (Parakeet + Gemma 4 E4B): ~200mb idle, ~300mb while speaking, brief spike to 4–6GB during transcription/polish, then back to 200mb CPU: ~0% idle, ~20% peak during use EDIT BTW, I develop it during my live streams from 8:30 am to 10:30 am ET everyday here. I show the code and decisions I make live on the stream. If you want to ask questions / push for some features / push to make it open source / etc. - join the stream, push for it in the chat and I'll consider it! Also, seeing the number of feedback, and feature requests in the comments I've decided to create a discord server to make sure that nothing will be lost and everything will be addressed. You can join here. submitted by /u/EfficientLetter3654 [link] [comments]

PSA: Claude Code's VS Code extension leaked my Supabase service-role key from a momentary text-selection in a file I'd already closed, into a brand new CLI session.

If anyone has 60 seconds to try the repro on macOS/Linux to confirm it's not Windows-specific, that would help triage a lot. I filed a bug on Claude Code's VS Code extension where selection state from a closed file persists into a new CLI session — including selections made just for clipboard copy-paste, not for AI context. Closed the file, opened a different one, started a fresh claude session in a terminal, and it reported back the previously-selected lines from the closed file. Repro steps and details: https://github.com/anthropics/claude-code/issues/58886 I'd selected two lines in `.env.production.local` to copy-paste a Supabase value into a dashboard — normal workflow. Then I closed the file, opened an unrelated TypeScript file, and started a fresh `claude` session in a new terminal to test something completely different. The first thing the new session did was tell me what was in the env file I'd closed, including both the publishable key and the service-role key. The IDE bridge had cached the selection past file close and served it to a session that should have been a clean slate. Rotated the keys immediately. Filed a GitHub issue with full repro: https://github.com/anthropics/claude-code/issues/58886 **60-second repro if anyone wants to confirm whether this is Windows-specific:** 1. Open any file in VS Code with the Claude Code extension installed. 2. Select two lines with recognizable values (e.g. `FOO=abc` / `BAR=def`). 3. Close the file tab. 4. Open a different file. 5. Open a terminal in the same VS Code window and run `claude` (no flags). 6. Ask: "what file is open in my IDE?" 7. Note whether it reports content from the file you closed in step 3. My setup: Windows 11, Claude Code CLI 2.1.138, VS Code extension 2.1.140, PowerShell in the integrated terminal. Would especially appreciate confirmations or non-reproductions from macOS/Linux users on the issue. A quick "reproduced on [OS]" comment on the GitHub issue moves Anthropic's triage queue more than upvotes. The narrower bug (selection persisting past file close) seems independently fixable from the bigger "should IDE auto-attach be opt-in" question that's been open since February in #24726. submitted by /u/SportSpecialist2536 [link] [comments]

Open-sourced reusable agent skills for your social media campaigns

Hi all! When creating new social media campaigns there are a few common steps you need to go through: strategy, planning, creation, review, approval, distribution and finally measurement and learning. I saw these common themes as an opportunity to create reusable skills for campaigns. This might help you come up with increasingly varied ideas (if you're using AI). These are the skills I created: /positioning: turns competitor gaps into an angle for your campaign /content-pillars: split your angle into 3 to 4 repeatable campaign themes /cadence-planner: plan how often you want to post /repurpose: turn your existing source material into post ideas for each theme /viral-hooks: turns each post idea into stronger openings that fit the platform /post-packager: creates post drafts with copy, asset, time, CTA, and review notes ready for one-click approval. /calendar-builder: build a scheduling-ready publishing calendar. Still iterating, so if there's a part of your workflow that's painful or that I'm clearly missing, let me know and I'll try to adjust it! submitted by /u/veganrunner95 [link] [comments]

Opus 4.7 Low Vs Medium Vs High Vs Xhigh Vs Max: the Reasoning Curve on 29 Real Tasks from an Open Source Repo

TL;DR I ran Opus 4.7 in Claude Code at all reasoning effort settings (low, medium, high, xhigh, and max) on the same 29 tasks from an open source repo (GraphQL-go-tools, in Go). On this slice, Opus 4.7 did not behave like a model where more reasoning effort had a linear correlation with more intelligence. In fact, the curve appears to peak at medium. If you think this is weird, I agree! This was the follow-up to a Zod run where Opus also looked non-monotonic. I reran the question on GraphQL-go-tools because I wanted a more discriminating repo slice and didn’t trust the fact that more reasoning != better outcomes. Running on the GraphQL repo helped clarified the result: Opus still did not show a simple higher-reasoning-is-better curve. The contrast is GPT-5.5 in Codex, which overall did show the intuitive curve: more reasoning bought more semantic/review quality. That post is here: https://www.stet.sh/blog/gpt-55-codex-graphql-reasoning-curve Medium has the best test pass rate, highest equivalence with the original human-authored changes, the best code-review pass rate, and the best aggregate craft/discipline rate. Low is cheaper and faster, but it drops too much correctness. High, xhigh, and max spend more time and money without beating medium on the metrics that matter. More reasoning effort doesn't only cost more - it changes the way Claude works, but without reliably improving judgment. Xhigh inflates the test/fixture surface most. Max is busier overall and has the largest implementation-line footprint. But even though both are supposedly thinking more, neither produces "better" patches than medium. One likely reason: Opus 4.7 uses adaptive thinking - the model already picks its own reasoning budget per task, so the effort knob biases an already-adaptive policy rather than buying more intelligence. More on this below. An illuminating example is PR #1260. After retry, medium recovered into a real patch. High and xhigh used their extra reasoning budget to dig up commit hashes from prior PRs and confidently declare "no work needed" - voluntarily ending the turn with no patch. Medium and max read the literal control flow and made the fix. One broader takeaway for me: this should not have to be a one-off manual benchmark. If reasoning level changes the kind of patch an agent writes, the natural next step is to let the agent test and improve its own setup on real repo work. For this post, "equivalent" means the patch matched the intent of the merged human PR; "code-review pass" means an AI reviewer judged it acceptable; craft/discipline is a 0-4 maintainability/style rubric; footprint risk is how much extra code the agent touched relative to the human patch. I also made an interactive version with pretty charts and per-task drilldowns here: https://stet.sh/blog/opus-47-graphql-reasoning-curve The data: Metric Low Medium High Xhigh Max All-task pass 23/29 28/29 26/29 25/29 27/29 Equivalent 10/29 14/29 12/29 11/29 13/29 Code-review pass 5/29 10/29 7/29 4/29 8/29 Code-review rubric mean 2.426 2.716 2.509 2.482 2.431 Footprint risk mean 0.155 0.189 0.206 0.238 0.227 All custom graders 2.598 2.759 2.670 2.669 2.690 Mean cost/task $2.50 $3.15 $5.01 $6.51 $8.84 Mean duration/task 383.8s 450.7s 716.4s 803.8s 996.9s Equivalent passes per dollar 0.138 0.153 0.083 0.058 0.051 Why I Ran This After my last post comparing GPT-5.5 vs 5.4 vs Opus 4.7, I was curious how intra-model performance varied with reasoning effort. Doing research online, it's very very hard to gauge what actual experience is like when varying the reasoning levels, and how that applies to the work that I'm doing. I first ran this on Zod, and the result looked strange: tests were flat across low, medium, high, and xhigh, while the above-test quality signals moved around in mixed ways. Low, medium, high, and xhigh all landed at 12/28 test passes. But equivalence moved from 10/28 on low to 16/28 on medium, 13/28 on high, and 19/28 on xhigh; code-review pass moved from 4/27 to 10/27, 10/27, and 11/27. That was interesting, but not clean enough to make a default-setting claim. It could have been a Zod-specific artifact, or a sign that Opus 4.7 does not have a simple "turn reasoning up" curve. So I reran the question on GraphQL-go-tools. To separate vibes from reality, and figure out where the cost/performance sweet spot is for Opus 4.7, I wanted the same reasoning-effort question on a more discriminating repo slice. This is not meant to be a universal benchmark result - I don't have the funds or time to generate statistically significant data. The purpose is closer to "how should I choose the reasoning setting for real repo work?", with GraphQL-Go-Tools as the example repo. Public benchmarks flatten the reviewer question that most SWEs actually care about: would I actually merge the patch, and do I want to maintain it? That's why I ran this test - to gain more insight, at a small scale, into how coding ag

Keep losing great answers in long Claude chats

I'm a heavy Claude user. for a while I had the similar problem that I saw other users in this subreddit have: Claude gives you a genuinely great answer buried somewhere in a 200 message conversation. The longer conversations get, the harder it becomes to find it again. The thing is, no AI platform solves this. Claude doesn't. my previous workaround was to manually copy and paste to Google doc. I even have a dedicated "Claude" bookmark folder. but bookmarking a conversation only tells you which chat, not where inside it. You still end up scrolling for minutes (even I am certain that the things i am trying to retrieve is somewhere in this super long chat). Ctrl+f is okay unless you remember the exact key words or the wording of a sentence, like how it was originally written. But more often it just the general meaning. paraphrasing is not enough because one word off, and Ctrl+F won’t find anything. So I built ChatVault. It is a highlighter for messages and text selections for Claude (but also works in ChatGPT, Gemini, and Perplexity). The idea is simple: highlight anything → tag it → find it later in a local, searchable knowledge base. You can organize those clips by project / by tag. I also built a function that allows you to jump back to the SPECIFIC location of your highlighted answer in a long chat. In a 20,000-word conversation, Claude's 14th response might have 8 bullet points, and only bullet #6 is the one you actually need. ChatVault lets you jump directly to that exact bullet point, not just the message, like those fluorescent flag tabs you stick on in a textbook. I hope this tool can help people to quickly navigate long conversations instead of scrolling through thousands of tokens. I personally use it to clip Claude's best explanations when studying, or save a really well-written bulletpoints that i want to refer later when writing. It supports not only Claude but also ChatGPT, Gemini, and Perplexity, so now I feel everything across different platforms comes together. Built this with Claude's help. Free to try. https://www.chatvault.dev/ submitted by /u/Embarrassed-Slip8094 [link] [comments]

Why I added a second Claude chat whose only job is to disagree with my first Claude chat

I've been tracking the cost of rework when AI-coding assistants (Claude Code mostly) hit ambiguous specs or canon violations they can't see. Across six production projects over the last several months, I noticed the same pattern: I'd draft a kickoff, fire it at Claude Code, it would either ask 2-4 clarifying questions mid-execution or come back with a Final Report that had 4-8 deviations from my intent. Some of those deviations were silent failures I didn't catch until later. The pattern I landed on: a second Claude chat whose only job is to read my kickoffs adversarially before they fire. I prime it with the project's canon (voice docs, state-of-project, lessons learned) and a protocol that tells it to find what's ambiguous, what's missing verification, what could fail silently. It returns a verdict in a predictable format — PASS, PASS-WITH-FLAGS, or FAIL — and I either revise the kickoff or fire it. Just closed out a phase using this. The numbers: - 9 audit gates across the phase - 2 FAILs, 4 PASS-WITH-FLAGS, 3 PASS - Zero clean first-pass PASS on customer-facing copy (the Architect chat is wrong on first pass by design — different role, different optimization target) - ~$33 in actual Claude Code API spend - The adversarial reviewer caught issues that would have cost an estimated $150-400 in additional Claude Code time to remediate - Several catches were silent failure modes (curly-quote drift in an HTML meta tag that would have shipped broken, a canon violation re-committed by my own Architect chat one kickoff after we locked the rule against it) I'm on Claude.ai Max 5x ($100/month flat fee), so the adversarial reviewer's marginal cost was effectively zero — it eats rate-limit budget, not metered dollars. For people running on API pricing, the per-gate cost would be in the $0.06-0.90 range depending on model. The ROI is concentrated entirely on the Executor (Claude Code) side, where retries are real metered API spend. The four roles I'm running: - Director (me): owns the project, gates ship decisions, adjudicates disagreements between Architect and Auditor - Architect (chat AI): drafts kickoffs, brainstorms, documents - Executor (Claude Code): receives kickoffs, executes, reports back - Auditor (separate chat AI): adversarially reviews kickoffs and Final Reports I've been calling the overall thing Calibrated Vibe Coding and writing up the canon publicly at github.com/kinestheticmarketing-stack/calibrated-design-canon — the manifesto, the standard, the Auditor protocol, the priming template, and the Phase 2.6 retrospective with the full breakdown of catches, costs, and friction patterns are all there. The Auditor protocol is the most immediately useful piece if you want to try it: METHODS/AUDITOR_PROTOCOL.md and METHODS/AUDITOR_PRIMING_TEMPLATE.md. You can spin up an adversarial reviewer on your next high-stakes kickoff and see if it catches anything yours missed. Anyone else running multi-role AI workflows? Curious what you're catching that single-chat workflows miss. submitted by /u/catnomadic [link] [comments]

Can we acknowledge that Anthropic watches open sourcers and copies them?

I’ve been seeing over the past few months an interesting phenomenon, an open sourcer makes a tool or MCP < Anthropic adds functionality for that exact thing a couple weeks later < repeat. The biggest examples are Openclaw (like 5 features, including cowork), persistent memory across chats, and latest example of the “goal” feature being added. This is obvious and I’m not really saying anything that’s revolutionary here, I’m sure we’ve all noticed it. My larger observation, no credit is given, they’re just copying and then providing a direct replacement for things open sourcers thought of. At this level, we’re all learning from each other. AI like it is right now is very new and you could even argue that they’re not copying, that we’re all just thinking the same things. The deeper issue though is that this shows a dystopian effect of AI, the big companies get the credit widely for things others have done. More people have heard about Claude cowork than have heard about Openclaw, and the result of the guy who made it was getting a job at OpenAI. He wasn’t able to make this into a business, it’s not how open source has been for the past 20 years where an idea can be copied but not completely absorbed. Ideas are being absorbed, the person who made it doesn’t get credit by the masses, then gets hired by the companies that take their ideas. Is this a bad thing per se? Hard to fully know yet but it creates a weird dynamic where anything you put out there about MCPs or AI is gonna be absorbed and you won’t get credit for it. What if this expands into other industries and professions? Is this something that would be good in the scientific field? Imagine if Newton discovered the laws of motion but he used AI to formalize the equations, the AI companies saw the chats, took the idea directly from him, and he gets no credit. We’re sprinting towards a future where all that exists is the big companies, they get the credit and make the decisions. Sounds a lot like we’re becoming the coal miners living in company towns again, not owning anything or getting any credit, just being a cog in the machine. Edit: grammar submitted by /u/TheOnlyVibemaster [link] [comments]