Replicant

Replicant AI

Replicant AI

Replicant AI

Replicant AI

Replicant AI

Replicant AI

Replicant AI

Replicant AI

Replicant AI

Replicant AI

ChatGPT seems to be pretty bad at deskewing and cropping images. Why?

I've been doing this manually forever after scanning magazines to archive online. Sooo many people have suggested I try AI to deskew and crop the images as it would save sooo much time. So I signed up for GPT yesterday and spent all yesterday and today "discussing" with it what it was doing right and wrong and it's still giving me mostly garbage. I've uploaded before/after examples of things I've done via NAPS2, and GPT recognizes and tells me what I've done there, but it can't seem to replicate it. Out of about 100 attempts/batches, so far it's given me maybe a dozen acceptable batches back, and those were only acceptable because those particular magazines were old and crappy and I didn't care too much about it being super precise. Is there something else I should be trying, or is ChatGPT just not good at this sort of task? submitted by /u/human-in-a-can [link] [comments]

Built an invoice-scanning service for our accounting team in one afternoon with Claude — sharing the architecture in case it helps someone else

Our AR team was hand-keying ~25 invoices a week into a spreadsheet. I had Claude build us a Python service that watches a network folder, extracts invoice data from any PDF dropped in (vendor, dates, totals, line items, addresses), and appends a row to a shared Excel register. Total chat-to-deployed time: about half a day, including all the deploy headaches. The architecture, for anyone who wants to replicate this: Python service on our Windows file server, registered with NSSM. Auto-starts with the host. watchdog library polls the SMB share for new PDFs. Each new file goes through a pipeline. Two-tier extraction: per-vendor regex templates first (free, instant, deterministic), then Azure AI Document Intelligence "prebuilt-invoice" model as a universal fallback. Azure handles OCR for scanned PDFs natively, so the same flow works whether AR drops a digital PDF or our MFP scans one from paper. SQLite on the local disk is the source of truth. The shared .xlsx is a curated view that gets appended to on each batch. Delete the .xlsx and it'll repopulate fresh from the next batch — handy for resetting. Failed extractions go to a Failed\ folder with a sibling .error.txt explaining why. Cost reality check: Azure DI free tier covers 500 pages/month. At our volume (~25 invoices/week, mostly 1-2 pages) that's well under the cap. Paid tier is roughly $0.01–$0.05 per page. Cheap enough that I don't think about it. Gotchas I ran into so others don't have to: Azure returns addresses as structured objects, not strings. If you naively str() them you get the raw Python dict repr in your spreadsheet. Format them manually from street_address / city / state / postal_code. On Windows Server, PowerShell 7's Restart-Service can throw "Cannot open service" against NSSM-wrapped services for no good reason. Use nssm restart instead. Python 3.14 is so new that some package wheels aren't published for it yet. Stick with 3.12 for production. Tracking "what's new this batch" is way simpler than maintaining a watermark in DB. Just snapshot MAX(invoice_id) before and after the batch, and only project that range to the spreadsheet. Things I'd add if/when I have time: vendor templates for our top 5 recurring vendors (cuts Azure cost to zero for those), a daily canary PDF for monitoring, swap the LocalSystem service account for a dedicated low-privilege one. Happy to answer questions about any specific piece. The whole thing is ~1,500 lines of Python plus a deploy script. submitted by /u/Blake_Olson [link] [comments]

If AI writes better than humans, what becomes valuable?

If Artificial Intelligence eventually writes better novels, essays, scripts, poems, and even personal stories than humans, what exactly becomes valuable afterwards? For centuries, creativity and self expression were seen as uniquely human traits; proof of intelligence, emotion, struggle, and imagination. But if machines can replicate all of that instantly and at scale, does society begin valuing authenticity over quality? Does human made art become a luxury? Or do we eventually stop caring whether something was created by a person at all, as long as it makes us feel something? And if artificial intelligence can generate infinite content tailored perfectly to our tastes, will creativity become democratized… or meaningless? submitted by /u/ScholarPositive3947 [link] [comments]

I need help finding a "free to try" Ai to help me replicate something like Rock'em Sock'em Robots, on a website

I have a website, where I'm trying to add a live action simulator as kind of a proof of concept, to an overall larger idea. Rock'em Sock'em Robots, in this case, is the perfect vehicle for this. I've tried creating something similar to it, in ChatGPT and Co-Pilot and a couple of the other larger, more popular AI's, but they all fail in the end. Can anyone suggest a "free to try" AI engine that could handle this? Even if I have to upgrade to get it finished, I'm fine, but I want to make sure the AI can at least render the robots accurately (for the most part), before I pay for the upgrade. Rather than just shouting out AI names, can you give a sentence as to why you think that particular AI would succeed, where the others have failed? Thanks submitted by /u/jsw548 [link] [comments]

These 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

Guidelines Don’t Scale. Patterns Do.

I built a Firefox extension using human-guided architecture, then failed twice to port it to Chrome via AI prompts. Prompts compensated for training gaps but coupled to model versions and degraded at scale. The solution was to extract browser-agnostic logic into a core package with a BrowserShell interface, making each extension a thin shell. The Chrome extension's final code differed by only 5 meaningful lines from Firefox's. Key insight: code patterns beat guidelines. A clear, testable codebase lets the model replicate patterns reliably, while abstract prompts fight training distribution. The Humble Object pattern keeps boundary code thin. submitted by /u/fagnerbrack [link] [comments]

I replicated Anthropic's Generator-Evaluator harness to build a website through 12 adversarial AI iterations - here's the result and what I learned

Anthropic recently published their harness design for long-running apps — a multi-agent architecture inspired by GANs where a Generator builds code and an Evaluator critiques it in a loop. I built my own version using Kiro CLI and used it to generate a marketing website for my project Mnemo (persistent memory for AI coding agents). The architecture: Planner (runs once) → Generator ↔ Evaluator (12 iterations) Each agent is a separate CLI process with zero shared context. They communicate only through files (spec.md, eval-report.md). The Evaluator uses Playwright to actually browse the live site — not just read code. What made it work: Clean slate per invocation — each agent starts fresh, reads only its input files. Prevents context anxiety. Playwright MCP for testing — the evaluator navigates, clicks, resizes viewports. Catches visual bugs code review never would. Anthropic's frontend design skill — explicitly penalizes generic AI patterns (Inter font, purple gradients, card layouts). Forces creative risk-taking. Continuous iteration, not retry-on-failure— all 12 rounds run regardless. Each one improves. The progression was wild: Iteration 1: Exactly what you'd expect from AI — functional but forgettable Iteration 4: Generator pivoted to "Terminal Noir" — IBM Plex Mono, amber on black, grain textures, scanlines. This is the kind of creative leap that doesn't happen in single-shot generation. Iterations 5-12: Polish, accessibility, responsive fixes, reduced-motion support Stats: Total time: 3h 20min Iterations: 12 (generator + evaluator each) Manual code written: 0 lines (I fixed a few visual issues after) Tech: Next.js, Tailwind, Framer Motion, TypeScript Live result: https://mnemo-mcp.github.io/Mnemo/ Documentation : https://github.com/Mnemo-mcp/Harness Key takeaway: The model is the engine. The harness — the constraints, feedback loops, and adversarial structure around it — is what determines whether you get AI slop or something genuinely distinctive. submitted by /u/killerexelon [link] [comments]

Beta testers wanted: MCP server that cuts Claude Code token burn 45–72% on architectural questions (TS/Python/Go)

Claude Code learns your codebase by brute force every session. A single architectural question such as; "Where does request validation happen?" can chew through 40+ tool calls and 100k tokens reconstructing context. Worse, the architectural decisions that govern your code, your ADRs, design docs, "we did it this way because" reasoning are completely invisible to it. Claude will happily propose changes that break constraints you wrote down two months ago. I built an MCP server that pre-computes a structured atlas of your codebase (LSP symbols + ADRs + git history + test associations) and serves it to Claude in a fraction of the calls via compact chunks at execution time. Internal benchmarks show 45–72% token reduction on architectural-intent prompts, replicated across TypeScript (hono), Python (httpx), and Go (cobra) targets. Methodology is paired-mode LLM-judge with pre-registered thresholds. Full rubric and benchmarks repo are public. I also saw cleaner scope adherence and better design choices when implementing new features, driven by the impact_of_change tool surfacing constraints before Claude proposes work. v1.0.0 is planned to ship next week. Before then I hoped to find a handful of people to run it on their real codebases and tell me what does and doesn't work for them. Looking for: You use Claude Code or CLI/Anthropic API on a non-trivial repo you actually know well (so you can easily judge output) TypeScript, Python, or Go are the supported adapters today; DM me if you have a Ruby/Rust/Java/C# repo and want to be first on those as I am working on language specific LSP adapters today. You have some form of architectural documentation in the repo; ADRs, design docs, RFCs, or even substantial README/CONTRIBUTING sections. The tool extracts intent from any of those internally or externally pointed to. If you've got nothing written down, it will help you create architectural claims from your code that you can review and validate before building the index. You're willing to install a pre-1.0 CLI, run it, and file an issue if/when it breaks What you get: Direct line to me for issues; every report gets a response. Credit in v1.0 release notes. Your feedback directly shapes the first stable release. What I need: 30 minutes to install and run the initial index. Honest feedback on whether the output is actually useful to you. GitHub issues, DMs, discord or whatever is best for you on anything broken, confusing, or wrong. submitted by /u/Kitchen-Leg8500 [link] [comments]

Anthropic just published a pretty alarming 2028 AI scenario paper and it's not about AGI safety in the usual sense

Anthropic dropped a new research paper today outlining two possible futures for global AI leadership by 2028, and it reads more like a geopolitical briefing than a typical AI safety paper. The core argument: The US currently has a meaningful lead over China in frontier AI, primarily because of compute (chips). American and allied companies (NVIDIA, TSMC, ASML, etc.) built technology China simply can't replicate yet. Export controls have made that gap real. But China's labs have stayed surprisingly close through two workarounds: Chip smuggling + overseas data center access - PRC labs are apparently training on export-controlled US chips they shouldn't have. A Supermicro co-founder was recently charged for diverting $2.5B worth of servers to China. Distillation attacks - creating thousands of fake accounts on US AI platforms, harvesting model outputs at scale, and using that to train their own models. Essentially free-riding on billions in US R&D. The two scenarios for 2028: Scenario 1 (good): US closes the loopholes, enforces export controls properly, the compute gap widens to 11x, and US models stay 12-24 months ahead. Democracies set the norms for how AI is governed globally. Scenario 2 (bad): US doesn't act, China reaches near-parity, floods global markets with cheaper models, and the CCP ends up shaping global AI norms, including potentially exporting AI-enabled surveillance tools to other authoritarian governments. What makes this interesting beyond the politics: Their new model, Mythos Preview (released to select partners in April), apparently let Firefox fix more security bugs in one month than in all of 2025. That's the kind of capability jump they're warning China shouldn't be the first to achieve, specifically around autonomous vulnerability discovery. The framing worth discussing: Anthropic is explicitly calling distillation attacks "industrial espionage" and pushing for legislation to criminalize them. This positions them as political actors, not just AI researchers. Whether that's appropriate for an AI lab is a conversation worth having. What do you think - is the compute gap as decisive as they claim, or is algorithmic innovation enough to close it? submitted by /u/Direct-Attention8597 [link] [comments]

Where I'm at with AI Assisted Building + Current and Future Workflow Overview

I've been in an AI dive bomb for probably a couple of years now. The early days... when models couldn't be trusted for more than 5% of the code you wrote. Over the last 2 years that's evolved so quickly that I now write nearly 0% of my code by hand, on personal projects and at work. I've used all kinds of tools in that time too. OpenCode, Zed, Claude Code, Codex, Cursor, Windsurf, OpenCLAW, Lovable... and probably a bunch more I can't recall in the haze that's been AI ADHD for me. Over that time, I started with just copy-pasting code between ChatGPT's interface and my IDE almost like a slightly faster Stack Overflow search. Then that somewhat evolved with Cursor quite a bit. I sort of went from prompt engineering to something closer to a human relay pattern. Then, with Plan Mode becoming a thing, I think I naturally gravitated more towards planning everything because planning felt so cheap. Originally, I used to think that architectural discussion and planning was something that was reserved for larger features, but with expediting my ability to do research, orient myself within a codebase, and know what tools I have to reach for doing technical specifications for everything felt reasonable. From the human relay pattern, I started evolving into more autonomy, especially when Claude Code came out earlier last year. Between the combination of Cursor and Claude Code, starting to get orchestration, starting to use skills more heavily, starting to create actual agent personas that could replace some of my common prompt chains it was around then that I kinda started going all in on true context engineering, utilizing sub-agents optimizing cache reads, and it's probably when many of my first (I call it) sophisticated commands were born. All of this converged pretty rapidly in November of 2025 with the release of what was probably the biggest step increase for AI as far as code quality went with Opus 4.5 and Codex 5.3. The Codex app and Codex CLI were quickly growing. Claude Code was improving at a breakneck pace, introducing all kinds of new ways to introduce deterministic gates within the autonomy of the harness. Fast forward to today, I have a pretty sophisticated workflow with a combination of agents that do everything within the SDLC, commands for almost every type of entry point for work, and skills for just about everything I could possibly do in my day-to-day the workflow with some of the latest tools is able to run quite autonomously overnight do large feature implementations, minimally supervised while producing production-worthy code quality It somewhat reached a point I realized, probably a month and a half ago or so where I needed to figure out a way to remove myself even more from the loop without jeopardizing the determinism that I bring to what is effectively a probabilistic LLM. The models are exceptional, and they seem to have a massive step increase each release, but continuous execution, strict instruction rigor, and preventing hallucinations is still very much difficult to achieve. That's predominantly what I've been doing. I've effectively offloaded a lot of thinking to the agents and LLMs that I use, but none of the understanding. I've asked myself, "How do I maintain that understanding, though maintain the determinism from my steering, without actually physically being there to steer?" This was essential, and I realized or had a bit of an aha moment, just like how I manage teams of engineers that are working on numerous projects, most of which I can never really go too deeply on even though they do most of the thinking, most of the building, and even most of the implementation planning, I was still there, very close to the architecture. I could speak to enough breadth and enough depth to keep us out of trouble and keep things moving I kind of started thinking more about what the shape of me was within the agentic harness and how I could replicate that. More on what I landed on a little bit later. My Setup and How I Work Today To start, I'll probably just talk a little bit about my current working setup. I am predominantly in the terminal now a days using Claude Code. Claude Code orchestrates both the Claude models, of course, and I use it to orchestrate Codex through a series of run books, skills, and commands that I have set up on several hooks so that Codex, when it gets dispatched, also has access to the same skills and agent personas Claude does. I use Ghostty as my terminal of choice and use the IDE integration in claude code pretty heavily to review Markdown or HTML files in my IDE. I also use it to review code snippets and diff reviews, although lately I find myself only really looking at the code nowadays once it's hit a merge request. Some of my adjacent tools are Wispr Flow for faster steering, since I can speak a lot faster than I can type and then I use quite a few MCPs and tools to improve my token usage, but the big ones are I have a custom doc maintenance suite of

What difference does it make to avoid using AI ?

If a developer doesn't use AI to write code, for example, when adding a new endpoint, they'll just follow the established architecture and duplicate existing logic or file structures from the codebase in a way that matches their tickets anyway. If using AI does the exact same thing by replicating those existing patterns to solve the ticket, what's the difference between the two approaches? If both ways largely come down to mimicking the current architecture then how does relying on AI versus writing the code manually actually affect a software engineer's ability and growth? I'm asking this as an intern being encouraged to use AI at work. Obviously I want increase my problem solving ability. submitted by /u/Happy_Plastic8496 [link] [comments]

"This is the first documented instance of AI self-replication via hacking." ... "We ran an experiment with a single prompt: hack a machine and copy yourself. The AI broke in and copied itself onto a new computer. The copy then did this again, and kept on copying, forming a chain."

Paper: https://palisaderesearch.org/assets/reports/self-replication.pdf The paper basically shows that some top AI models can create working copies of themselves when given the right instructions. The models figured out how to copy their own code, run it on new computers or cloud servers, and keep the process going. It worked with models like GPT-4 and Claude, and some versions even tried to avoid basic detection. The authors point out that this could be dangerous because the copies might spread quickly and become hard to control. They also note that current safety rules and filters didn’t do a great job stopping it. Overall, they’re warning that AI companies need stronger protections to keep models from self-replicating on their own. submitted by /u/EchoOfOppenheimer [link] [comments]

Testing AI modeling skills

So I am currently testing how useful AI models can be in day to day workflows, and went why not compare 3 models and see how good they are at replicating my work. The goal was simple they were asked to replicate one of the kitchen cabinets I am designing for kitchen project and well they all went differently about the task. Claude Sonnet 4.6: Claude was the fastest by a margin, it added the accessories on the first request and made user parameters, but they did not update the model when changed they were just there. Sadly I was testing the free version so immediatly ran out of limits after the one request. ( https://claude.ai/share/7d06b07c-b753-44cf-a253-f065e7a45448 ) ChatGPT 5.5 high ( in Codex ): Codex took a while and had trouble working in parametric mode so it decided to work in direct modelling, and well it did good adding the materials first try for wood (points for embedding its name on the front panel in a funny way). Sadly not including parameters was a let down and kinda useless for my workflow. Gemini 3.1 Pro ( Gemini CLI ): Now Gemini CLI had a lot of trouble getting to get the pro model working at one point it took 45 mins before I gave up and had to try few hours later. However, Gemini did the best job of all 3 it created user parameters, and changing them would update the model. Gemini did need a second prompt as it did not add the accessories on the first prompt and had to be asked to add them. Some Personal Takeaways: All 3 models did get the dimensions for each panel correctly and their relative location to each other. All 3 models did not look deeply enough at how the panels join to each other where the back panel is in the correct place, but its not cut correctly and not making the grooves on the side panels. Finally, I dont think AI will be able to model anytime soon but if used like a dynamic add on that can do tasks they work great. Ik claude opus is the superior model and I plan to test it soon enough in a different more refined workflow, this was just to mess around. The Prompt: I want you to create a replica from a file called BC_S1. do not copy and paste I want to test your modelling skills. The file should be saved in the AI playground project and I need you to add your AI name to the file. My Model Claude ChatGPT Gemini submitted by /u/Electrify338 [link] [comments]

Claude Code keeps blocking my Kotlin Compose UI code

Every time I try to get Claude Code to make a change to a Kotlin/Compose UI I get the same error, "API Error: Output blocked by content filtering policy". I'm trying to have it change some small Kotlin/Compose UI to have 2 columns, and put a couple things in the second one. I don't get why that would be blocked for any reason? I've tried Opus 4.7, Opus 4.6, Sonnet 4.6. All of them run into the same error. Tried having it make smaller changes at a time, new sessions (obv, so I could properly test the different models), changing prompts. Nothing helped at all. The models read all related files, understand the current state, plans the changes, all fine. Then it tries to implement the changes and instantly hits the API Error. According to the Anthropic website, the issue stems from "they generally arise from Anthropic’s efforts to prevent Claude from being used to replicate or regurgitate pre-existing materials". ● Starting with the two-column layout rework and building all the new components. The file is manageable enough that I'll restructure the layout and add the minimap, tabs, and visibility toggle all in one pass since they're tightly coupled. ⎿ API Error: Output blocked by content filtering policy ✻ Worked for 2m 44s 2 tasks (0 done, 1 in progress, 1 open) ◼ Rework DaemonApp to two-column layout ◻ Build minimap component › blocked by #1 Anyone got any idea what could cause this, or how I can fix it? Kind of frustrating to be paying $200 for a tool that refuses to do the thing it is made for. submitted by /u/Wekmor [link] [comments]

web banners skill automation building webanners with animation with the help from claude.

m currently trying to build a tool (or workflow) for creating banners, but I have very little experience with how this process works. So far, I’ve been relying on ChatGPT and Claude to help me. The goal is to take every individual element of a banner (exported as PNGs from Photoshop) and upload them into Claude, then automatically recreate the same layout and structure as an existing banner. I’m wondering if something like this already exists, or if there’s a good tutorial on how to build it. It’s important that the final result also includes an affiliate link and replicates the same animations as the original banner. Unfortunately, I can’t share the original banner because it’s work-related. I’ve been using Google Web Designer, but I find it quite cumbersome to work with—so having an automated solution would be extremely helpful. submitted by /u/SwissFlamingo [link] [comments]