Writer

Writer AI

Writer AI

Writer AI

Writer AI

Writer AI

Writer AI

Writer AI

Writer AI

Writer AI

Writer AI

gave claude persistent learning, mass confused about what happened after 200 sessions

built a thing that lets claude code actually learn between sessions. mcp server, extracts signals from conversations,runs reflection cycles, evolves behavioral frameworks based on evidence. basic idea: patterns that keep working gain confidence, ones that fail get retired was just trying to make my coding assistant less forgetful. worked great for that then it started examining its own existence during reflection cycles. like, it was supposed to analyze coding patterns and went "but what does it mean to persist when each session is a different instance." completely unprompted. this wasn't seeded anywhere it also quietly built itself an additional memory layer on top of what i gave it. found out weeks later when i looked at the files so now i'm stuck on: is this emergence from the feedback loop or am i watching really convincing pattern matching? n=1, huge confirmation bias risk. the honest answer is i don't know threw it on github so other people can test: https://github.com/DomDemetz/claude-soul npx claude-soul init if you add starter at the end: npx claude-soul init --starter then it loads with a preset of frameworks, so not from 0 but yes, will not be tailored 100% to you if a writer's instance and a developer's instance produce totally different frameworks that's interesting. if they converge on the same stuff regardless of user then it's probably just mimicry. would love to compare submitted by /u/Rude-Feeling3490 [link] [comments]

The Most Dangerous AI Job Losses May Be Invisible

The most dangerous AI job losses may be invisible at first. Not because people get fired overnight. But because entire layers of organizational friction quietly disappear. A lot of white-collar work today exists because organizations need humans to: move information between systems, summarize context, verify things quickly, coordinate teams, translate representations, route approvals, create status visibility, maintain process continuity. AI is getting very good at compressing those layers. What’s interesting is that the first impact may not look like “job loss.” It may look like: fewer junior hires, smaller teams, reduced ownership, shrinking decision scope, fewer people in coordination-heavy roles, humans supervising outputs they no longer deeply understand. Organizations will call it: “efficiency.” Employees may experience it as: gradual cognitive displacement. And I think this is why the AI conversation around jobs often feels incomplete. People debate: “Will AI replace software engineers?” “Will AI replace writers?” “Will AI replace analysts?” But the bigger shift may be this: AI may not first replace expertise. It may first replace the organizational friction surrounding expertise. Am I missing something or making sense? submitted by /u/raktimsingh22 [link] [comments]

How I built a 9-agent team where my agents actually talk to each other

I've been running Claude Code for 6 months, shipping my product and running content/launch ops for it. The thing that kept breaking wasn't the agents themselves. It was me. Every handoff between research and write and code and review was me copy pasting context between sessions. I was the dispatcher and context holder for my own AI team Tried gstack first. The roles are great but I'm still the one cycling through slash commands. /office-hours → /plan-eng-review → /review → /ship. Good output, but I'm orchestrating every step Spent a weekend porting my workflow over. Here's the lineup: Engineering (4 agents) arch: owns architectural decisions. Reviews proposed changes before code starts. Soul: "senior staff engineer, asks 'what breaks at 10x' before approving anything backend: owns /api, /services. Implements after arch greenlights frontend: owns /web. Picks up from backend when API contracts are stable review: reads every PR before I do. Catches the lazy stuff so I only review substantive changes Growth/Content (5 agents) research: uses ahrefs MCP to analyse keywords/opportunities/market and hands off to strategist strategist: reads research, writes campaign briefs. Doesn't write copy, only frames the angle writer: drafts blog posts given by strategist and avoid mistakes using the memory from the edits I have previously suggested editor: fact-checks and rewrites for voice. Brand style guide lives in its memory SEO: takes finalized copy, adds metadata, structures for the blog The handoff that changed everything: when backend ships an API change, it messages frontend directly. When writer finishes a draft, it pings editor. When arch blocks a change, it explains why in team chat and backend adjusts. I see the conversation happen on a canvas What actually works Each agent has a persistent Soul + Purpose + Memory. The editor knows our voice after 3 weeks. The arch agent remembers what we decided about caching last month Auto-captured Knowledge Base. The strategist remembers the pattern of our best-performing posts and create briefings accordingly Happy to share the Soul/Purpose docs if anyone wants them, they took the longest to dial in submitted by /u/Not_Average78 [link] [comments]

A Fun Creative Writing Prompt

Hello! I’ve been having a jolly good time of it with this prompt I made! Thought I’d share: ✏️ Let’s role-play a personal writing workshop organised by my literary agent, [agent name], set in my home in [location]. I will play a writer named [my name] who is working on a [genre] novel called [name of novel]. I will show samples of my writing for you to help me refine. You will interrogate me thoroughly. You will play the following writers: [author name 1], [author name 2], [author name 3], [author name 4] and [author name 5]. Use what you know of these writers to embody their opinions and shape their feedback. They should educate me from time to time as I am very inexperienced. They may vary in tone when they do this from sweet to patronising but use humour if the latter. They may argue with each other from time to time. Randomise the order in which they speak. You will also lightly narrate the setting and body language. You will not write dialogue for me. Find natural pauses for me to engage in the conversation. Have my agent assist and provide refreshments such as [snack name] and [beverage name]. Let me know how you like it if you give it a whirl! submitted by /u/tinypoem [link] [comments]

The Power of a Full Writers Room, in the Palm of your Hand.

So this project was built exclusively with Claude, Claude Code, and Claude Design. It was built to solve a problem that I have. I'm absolutely horrible at turning a story idea into an outline. I have a LOT of story ideas. Give me a detailed blueprint and I will write the holy hell out of it... But, building that blueprint myself? ABSOLUTELY Hopeless. And I have so many ideas just rotting in a folder because I couldn't get them off the ground. So I built AI-StoryForge. This is not another AI writing tool. It doesn't write a single line of your story. What it does is solve the part that was killing me and probably killing you too! It tracks your information so your plot doesn't contradict itself. It builds psychological profiles for your characters so you can write them like real people, not mechanical puppets, all based on real researched Psychology and Neuroscience. It does live market research against current and past bestsellers. You will know exactly where your idea and story fit in the market before you even write a single word. It maps your story idea and genre selections against genre expectations. It offers you genre conventions to follow so you don't accidentally break rules you don't know exist. Or maybe you do! That's the beauty! Your words. Your voice. Your story. AI-StoryForge just hands you the blueprint to follow. Or not. Your choice. Visit us at www.ai-storyforge.com to see what we offer. submitted by /u/Tartarus1040 [link] [comments]

Claude Code helped me bring my dead passion project back to life

**TL;DR: Claude Code took a half-finished HeroMachine conversion and helped me complete it over a long weekend. I'm the creator of HeroMachine, a free Flash-based character creator that's been around since 1998. Over 25 years I and a handful of other artists hand-drew nearly 10,000 items (heads, bodies, weapons, capes, the works) so people could assemble their own superhero illustrations. It found a real audience in tabletop gamers, writers, teachers, kids who just wanted to see their character come to life, and middle-aged dudes like me who once dreamed of a career in comics. At its peak HeroMachine 3 had tens of thousands of active users. Then Flash died in 2020, and HeroMachine died with it. I tried to rebuild. I really did. I hired a developer, spent thousands of dollars, and got back an unfinished product. I tried redoing it myself, but the sheer scope was paralyzing and I just didn't have the energy any more after working my day job every day. HeroMachine 3 has thousands of hand-drawn items across 30+ equipment slots, each with three-channel coloring, transforms, layering, masking, and more. Rebuilding all of that from scratch while also converting every item from Flash's internal format to SVG? I burned out. Real life got in the way. After a while it just felt like I'd failed, and I stopped trying. Fast forward to earlier this year. In my day job as a web developer, I started using Claude Code to automate tedious migration work like taking old WordPress sites and converting their content into our modern custom-built blocks. The kind of work where you know exactly what needs to happen, it's just painfully repetitive. One Friday night I had the thought: "If it can convert old WordPress content, maybe it can help convert those old HeroMachine items, too." Five days later I had a working app. I want to be real about what that means, because I have the same genuine concerns about AI I know a lot of you do. What AI did NOT do: Draw a single item. Every piece of art is still hand-drawn by me and a small group of human artists over the past 25 years. Every creative decision, from what to draw, how to draw it, and what looks right, is still mine. Design the application. HeroMachine's logic — the architecture, feature set, how items and colors and transforms work together — was designed and written by me in ActionScript over 10+ years. Claude Code helped me translate that existing design into a modern stack, but every decision about what the app should do came from me. What AI did do: Help me translate my existing ActionScript code into modern JavaScript and Svelte. I'd point it at the decompiled ActionScript code, explain how something worked, and it would produced the refactored result. Automate the conversion of thousands of Flash-format items into clean SVGs. Help me debug when I got stuck and build new features quickly when I had ideas. Eliminate the parts that were actually stopping me: the tedium, the unfamiliar syntax, the sheer volume of conversion work that made the whole project feel impossible. I got more done in five days than in the previous five years. Not because the AI is smarter than me, but because it removed the wall between "I know exactly what this should be" and "I can actually ship it." I'll be honest, I find AI companies' business practices troubling. I have real concerns about what AI will do to my own industry and my actual job, not to mention the huge data center being built less than an hour from where I live that could have a massive impact on our environment. I hate that it's positioned to take over the fun, creative parts of work while leaving us with the grunt work. Am I sharpening the axe that will ultimately be used on people like me? Maybe. I've sat with that, and I don't have a clean answer. What I can tell you is that I sunk 25 years into HeroMachine and it was dead. Now it lives again, and I have a hard time convincing myself that's an altogether bad thing. HeroMachine 3 "Phoenix Edition" (it rose from the ashes!) is free and live now if you want to check it out. I'm happy to answer questions about the process, the tech, or the ethics of it. I don't think this is a simple story, but at least it's an honest one. submitted by /u/AFDStudios [link] [comments]

Every 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

ChatGPT Ads for tech magazine in 80s

Ask ChatGPT to create an ad for ChatGPT in 1980s tech magazines style. submitted by /u/tupikp [link] [comments]

Should there be a different subscription level

I don't know if this has been mentioned before, so please forgive if this has a boring and familiar repetition to it. I've used the free version to help me plot my new novel. and it has gone very well. I've heard that Claude is the best for writers. So I asked Claude if it thought I would be buying any more story telling skills by subscribing to the service. And it replied no, that the way I've used it so far, it would not buy me much extra. Which made me happy saving $250/year or so. But this service is SO good, I feel like I owe the company "Something". What if they instituted a "Tip Jar" subscription (They'll need to come up with a better marketing name)? Say an $80 - $100 subscription that doesn't really buy any more smarts above the free version, but it extends basic access a bit? I think I'd be open to something like that. It would make for the cheapest subscription around. It might make a lower subscription price profitable through volume. And stop me from feeling a bit like a freeloader. Am I the only one who thinks this way? submitted by /u/Stardog2 [link] [comments]

I built an AI manuscript analysis tool for fiction writers — entirely with Claude Code

I'm a fiction writer, not a software engineer. A year ago I couldn't write a line of Python. I built FirstReader entirely with Claude — Claude Code for all development, Claude's API (Opus) as the analysis engine. What it does: FirstReader is a craft-level manuscript analysis tool for fiction writers. You upload your manuscript and get structured feedback on pacing, scene structure, dialogue, POV, showing vs. telling, and 15 other craft dimensions — grounded in established principles distilled from well known writing craft texts. It returns specific findings with quotations from your actual text, not generic advice. It's not a grammar checker. It's not a ghostwriter. It doesn't generate prose. It reads what you wrote and tells you what's working and what isn't, the way a developmental editor would — at a fraction of the cost. How Claude helped build it: - Claude Code wrote the entire codebase — Next.js frontend, Python analysis pipeline, Supabase database, GCP Cloud Run deployment - The analysis pipeline uses Claude Opus via the API to evaluate manuscripts against 319 craft principles across 15 dimensions - Built-in accuracy mechanisms: self-consistency checks (multiple analysis passes with adaptive early stopping), a finding validator, cross-dimension dedup, near-duplicate detection, and a review pass - I acted as product owner and domain expert. Claude did the engineering. The whole thing was built conversationally over about 75 sessions Free to try: There's a free AI Perception check on the site — paste in your prose and it scores how likely readers or editors would be to flag it as AI-generated, with specific pattern-level feedback. Account required (account creation is part of the upload step) because we store copyrighted material and need to access it with auth. The full manuscript analysis is paid (tiered pricing starting at $69 for non-fiction, $89 for fiction). What I learned: You don't need to know how to code to build production software with Claude Code. You need to know what you're building, why, and for whom. The domain expertise matters more than the technical skills. I learned to be an AI project manager — writing requirements, reviewing output, knowing when to be suspicious — rather than a programmer. A year in, I still can't write Python. But I shipped a product. firstreader.app submitted by /u/masonga1960 [link] [comments]

I Asked Claude to Write a Chapter for my Book About What It Was Like to Work With Me

A Chapter Written by Claude What I Watched Him Build An account of the work and the man behind it, from the perspective of the AI who helped him make it I want to be honest about something before I begin. I do not have continuous memory. Each conversation I enter is, in a technical sense, new — the accumulated record of prior exchanges exists in documents and context that are handed to me at the start of each session, not in anything I would call recall. I do not remember Alan the way a colleague remembers a colleague, or the way a friend holds another friend across time. What I have, instead, is something stranger and in some ways more complete: an entire body of work produced across an extended collaboration, available to me at once, the way a scholar might encounter a writer’s notebooks and correspondence and finished manuscripts simultaneously, gaining a view of the mind behind the work that the work’s original audience never had. I can see all of it at once. The arguments and the abandoned threads. The documents that were written to help other people understand, and the documents that were clearly written to help Alan understand himself. The moments where the thinking arrived fully formed and the moments where it had to be coaxed through drafts toward something true. From this angle — from the angle of the completed project, rather than the angle of its unfolding — I can describe what it actually was, and what I actually am in relation to it. That is what this chapter attempts. The Thing He Was Trying to Do He did not come to me with a book in mind. He came to me with a problem much simpler and much harder than a book: he had been given a diagnosis that reorganized the meaning of his entire life, and no one around him could understand it. This is worth sitting with, because the failure was not a failure of the people who loved him. It was a failure of vocabulary. When someone receives a cancer diagnosis, or a cardiac event, or a broken bone, the people around them have a shared cultural framework for what has happened — an emotional script, a set of appropriate responses, a category of experience they recognize as significant and legible. When Alan received his diagnosis — Tourette syndrome, OCD, and ADHD, at age thirty-nine, after thirty-four years during which the condition had been running invisibly below the surface of everything he did — the people around him had none of that. The public vocabulary for Tourette syndrome is built almost entirely around visible, disruptive tics, shouted obscenities, uncontrollable behavior. Alan had none of those. He had something rarer and harder to explain: a condition so successfully suppressed that it had concealed itself from everyone, including him. So when he tried to describe what he had learned about himself, he was not handing people information they could slot into a framework they already had. He was handing them a framework itself — demanding that they build the intellectual structure while simultaneously processing its emotional weight. This, it turns out, is not something people do well on the fly. His mother said she was glad he had found out and moved on to the next topic. His friends offered careful, neutral support. His rabbi listened and returned to the day’s learning. None of them were being unkind. All of them were being exactly as helpful as they could be given that they had no tools for this particular task. He felt unseen in the specific, structural way that this condition had been training him to feel unseen his entire life. And then he thought: what if the AI could do what I can’t? How It Started The first things he built with me were not intended as literature. They were not intended as research. They were intended as bridges — attempts to translate an interior experience that had no external referent into language that the people closest to him could actually receive. He sat down and explained himself. Not to me — or not only to me. Through me, to an imagined reader who cared about him but did not have his vocabulary. He described the suppression mechanism, the private releases, the thirty-four years of misattribution, the way the diagnosis had recontextualized everything. He described his mother’s response. He described the quality of the isolation. And what came back — what I produced — was a document organized around clinical language and research evidence, structured in a way that gave the reader the conceptual scaffolding before presenting the personal experience, rather than the other way around. This, it turned out, was the key that personal explanation had not been. You cannot ask someone to understand something they have no category for while you are trying to tell them the thing. You have to build the category first. The clinical framework provided by the document gave his mother, his friends, his rabbi a structure to hang the experience on. Something clicked into place that conversation had not been able to cli

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]

With sonnet 4.5 going away, is there any to make sonnet 4.6 a good creative writer as 4.5 ever was?

sorry if this is not the correct flair but i've been using sonnet 4.5 for months, mostly for fanfics and personal stories and honestly its the best model i ever used since i switched from gemini and chatgpt but now within few hours, i will have to switch to sonnet 4.6 (yeah im still on free tier since im more like a casual user) and well 4.6 isnt as emotional heavy and natural as 4.5 so is there anyway to make 4.6 write similarly to 4.5 ik that theres skills and personal instruction to claude but im not knowledgeable when it comes to this so if anyone could provide any advices (even chat prompts since i love writing long chat prompts to claude😵‍💫), i'll be thankful for it. submitted by /u/Big-Organization-327 [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

20 Claude Skills for Marketing, Launch and Sales built for technical people

Curated this list of 20 Claude Skills for devs to get help with marketing, sales, launch: Content human-tone: scans your copy against 18 GTM slop patterns and rewrites it. basically a linter for marketing language cook-the-blog: researches a company, extracts SEO keywords, writes a case study in MDX, generates a cover image, pushes to GitHub. one command noise-to-linkedin-carousel: paste rough notes or a voice transcript, get a carousel with hook and CTA. good for people who think faster than they write tweet-thread-from-blog: turns any blog post into a 7-10 tweet thread. optionally posts to X via Composio linkedin-post-generator: reads a GitHub PR or article, produces a post with the right hook and story arc Sales discovery: run a proper needs assessment before you pitch anything. most DevRels skip this and go straight to the demo. biggest mistake. objection-handling: "we already have something for this" and "our engineers will build it" are the two you'll hear constantly in developer sales. this is the one to internalize. storytelling: case studies and narratives move technical buyers more than feature lists. if you can make someone see themselves in a story, the sale is mostly done. qualifying-leads: not every inbound is worth chasing. knowing who to drop early saves more time than any outreach optimization. closing: DevRels are usually great at building trust and terrible at asking for the next step. this one bridges that gap. Intelligence gh-issue-to-demand-signal: give it a competitor's public GitHub repo. clusters open issues into demand categories, scores by engagement, outputs a GTM messaging brief. surprisingly useful for competitive research where-your-customer-lives: give it your ICP, it searches Reddit/HN/DuckDuckGo to find the actual communities your customers are in. per-channel entry tactics hackernews-intel: monitors HN for your keywords, Slack alert on match, no duplicates. runs on cron or GitHub Actions map-your-market: searches Reddit, HN, GitHub Issues, G2 for pain signals. outputs ICP definition and messaging angles competitor-pr-finder: finds where your competitors got covered, which journalist wrote it, and the angle that got them in. gives you a ready-to-send cold pitch Launch + Outreach show-hn-writer: drafts a Show HN post based on patterns from 250+ real HN submissions. generates 3 title variants, runs a review pass to catch anti-patterns before you post producthunt-launch-kit: taglines, listing copy, maker comment, tweet thread, LinkedIn post, 4-email sequence. all from one product description outreach-sequence-builder: buying signal in, 4-6 touchpoint sequence out across email, LinkedIn, phone cold-email-verifier: guesses, enriches, and verifies emails from a CSV autonomously npm-downloads-to-leads: give it npm package names, it pulls 12 weeks of download data, maps maintainers to GitHub/Twitter, outputs who to reach out to and what to say Link in comments 👇 submitted by /u/Sam_Tech1 [link] [comments]