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AI governance for business’

I work at a fast-growth scale-up in a heavily regulated industry and there’s a huge internal push to ship self-service AI tools across teams. One simple example: build an AI email copywriter that lets our CRM team generate segmented campaign copy on demand, without brand or creative review. On paper, I get it. Speed, scale, autonomy. But before I do, a couple of questions I have in my mind are: - Who owns the output? If the CRM team generates 500 emails a week, and one of them is misleading, or just bad — is that on me? On them? On no one? - We have no AI policy. Yet we’re being asked to build tools that will produce customer-facing content at volume. -The “I built the system” defence feels thin. If I architect the email copywriter and hand it over, I’m implicitly endorsing everything it produces — but I have zero visibility into what’s actually being sent. This isn’t really about AI quality. Modern LLMs can write decent copy. It’s about accountability, brand risk, and what governance actually looks like when creative output becomes self-serve. I’m looking for advice on how are you handling this? Have you found a middle ground between enabling speed and maintaining standards? Did your company build a policy first, or did something have to go wrong before anyone took it seriously? Genuinely curious how others are drawing the line. submitted by /u/Medical_Traffic6417 [link] [comments]

Augmented Equivariant Mesh Networks for Anatomical Mesh Segmentation (ICML 2026 Workshops) [R]

Paper: https://arxiv.org/abs/2605.08172 Workshops: AI for Science & Structured Data for Health at ICML 2026 Abstract: Anatomical mesh segmentation requires models that operate directly on irregular surface geometry while remaining robust to arbitrary patient pose and mesh resolution variation. Existing task-specific mesh and point-cloud methods are not equivariant, and can degrade sharply under test-time perturbation, for example dropping by 25-26 IoU points on intraoral scan segmentation at 40o tilt. We present EAMS, an Equivariant Anatomical Mesh Segmentor built on Equivariant Mesh Neural Networks (EMNN), and evaluate it across four clinically distinct tasks spanning edge-, vertex-, and face-level supervision. We combine intrinsic mesh descriptors with anatomy-aware priors, including PCA-derived frames for dental arches and liver surfaces, and augment message passing to provide lightweight global context. Across intracranial aneurysm and intraoral segmentation, EAMS variants are competitive with specialized baselines on unperturbed inputs while remaining stable under geometric perturbations, and on liver surfaces they expose a favorable trade-off between canonical-pose accuracy and rotation robustness. These results show that a lightweight (<2M parameters) equivariant framework can deliver robust anatomical mesh segmentation across diverse supervision types without task-specific architectures. Hi everyone I’m excited to share my solo paper "Augmented Equivariant Mesh Networks for Anatomical Mesh Segmentation" which has been accepted for poster presentations at the ICML 2026 workshops on AI for Science and Structured Data for Health. The project stemmed from my parallel research on structural encoders for biomolecules where enforcing roto-translational equivariance is standard. In this work, I wanted to extend those principles directly to various 3D medical meshes. While current anatomical mesh segmentation methods are highly disjoint and anatomy-specific, we present a unified framework built on EMNN. By augmenting standard local message passing to incorporate a lightweight global context, and using a descriptive feature set incorporating intrinsic surface descriptors (HKS) and anatomical frames derived from an area-weighted PCA, we successfully benchmarked this single architecture across clinically distinct tasks spanning vertex-, edge-, and face-level supervision. Equivariance trade-off One of the more interesting findings from the experiments is that strict equivariance isn't always better. In fact, the inductive biases of the equivariant architecture occasionally performed worse than standard, non-equivariant baselines. For instance, on our liver dataset, the target anatomical landmarks are highly subtle creases. Standard baselines can "cheat" by using raw coordinates to easily resolve the left-right and front-back ambiguity. Because the equivariant network is mathematically blind to absolute space, it struggled with these subtle, asymmetric features. Future directions To fix this without losing the generalization benefits of geometric deep learning, I’m currently exploring relaxed constraints like learned canonicalization and frame-averaging (soft equivariance). As this is a solo project, I would appreciate any feedback! Also, I'll be heading to Seoul for ICML 2026 to present these workshop posters. if you're working on geometric DL for medical/biological applications, feel free to connect! submitted by /u/m0ronovich [link] [comments]

Looking for brutally honest feedback

TLDR: skip to elevator pitch, rip it to shreds, tell me why it's dumb. I'm a vibe coder. I find myself constantly feeling two things: uncontrollable excitement about being able to build functional apps, and constant fear that the apps I'm building with LLMs are a security disaster. I'm convicted the latter is true, and terrified that I have no way of knowing. I find this tension to be really upsetting. Something that promises to democratize application development for the masses is at the same time catastrophically increasing the number of applications deployed with huge security gaps baked right in. I asked Claude what I could do to ensure that the things I build for my own personal use are as secure as possible (within reason... I don't have much money for audits / etc). I've been deploying things to cloudflare so far, built with a mostly Typescript repo with a tiny bit of CSS and HTML. The conversation slowly led to me asking how a real developer would build things if security was their top priority. Claude got to the point of describing what it says are the architecture patterns and posture of top financial institutions, intelligence agencies and defense contractors. I asked it to ignore the hardware elements (high security on prem server requirements, hardware login keys, etc) and focus on the things that can be coded. That led to an idea which it summarized in the elevator pitch below. My concern, and the question here, is that it's just validating my silly vibe coder ideas and that the conclusion of the conversation is just nonsense. So, I was hoping to ask you all for as brutal a level of feedback as you can offer. If this is a dumb idea, please tell me, but if you don't mind, tell me why. Worst case, I learn something. Best case, maybe it's not a dumb idea. Or, Claude was blowing smoke up my... when telling me that it's a "novel" idea. I have no clue whether it is, or whether something like this already exists that I should've been using all along. Or maybe there's another answer (besides going back in time and doing a computer science / engineering degree like I now wish I had) that solves the problem I have. Anyway, here's the Claude generated (3rd redraft...) elevator pitch: A proposal for an open-source, pre-integrated application scaffold that provides security-hardened defaults for authentication, authorization, encryption, audit logging, input validation, and infrastructure configuration. The package would be designed for deployment and configuration through LLM-assisted workflows, targeting developers who build functional applications with AI assistance but lack the security expertise to identify or implement protections against common vulnerability classes. Core mechanism: A deployable foundation consisting of three integrated layers. The infrastructure layer uses Terraform or Pulumi modules to deploy a hardened environment: network segmentation, TLS termination, secrets management via HashiCorp Vault, internal certificate authority via step-ca/cert-manager, mutual TLS between services, PostgreSQL with encryption at rest, pgAudit, and row-level security enforcement, and container policies requiring signed images and non-root execution — scanned against CIS and HIPAA benchmarks via Checkov. The application layer is a project template (Go or Rust, with tradeoffs unresolved) providing pre-wired middleware: OpenID Connect authentication via Keycloak, attribute-based access control via Open Policy Agent or Cedar, schema-validated inputs, CSRF protection, security headers, rate limiting, and append-only audit logging with cryptographic hash chaining. Routes require authentication by default; bypassing requires explicit opt-out. The CI/CD layer is a pre-configured pipeline running Semgrep, Trivy, Checkov, cargo-audit, and Sigstore image signing on every commit with no developer configuration. Developers clone the scaffold, configure it, and build business logic inside it. Security controls are structural, not optional. Design constraint: The configuration surface, error messages, and documentation must be legible to both humans and LLMs, such that an LLM operating with the project context loaded produces chassis-compliant code by default. submitted by /u/Osiris1316 [link] [comments]

If you ask the model to validate your idea, it probably will

One underrated risk in the "AI for founders" discussion is confirmation bias with a research engine attached. If you ask a strong model to validate your startup idea, it can usually produce a convincing case. Market tailwinds, TAM estimates, competitor gaps, user personas, the whole thing. None of that means the idea is good. It may only mean your prompt pointed the model toward a flattering answer. The more capable the model gets, the more dangerous this becomes. A weak answer is easy to distrust. A polished memo with numbers and citations feels like diligence even when it is just your bias wearing a suit. I have started doing the opposite first. Ask for the strongest case that the idea is bad. Ask which customer segment would never buy. Ask what existing behavior proves the pain is not real. Then, only after that, ask what would have to be true for the idea to work. Tools can nudge this, but only a little. I have been doing a pre build planning pass first, sometimes in Verdent, sometimes just in a doc. The key is the instruction itself: do not help me feel right, help me find where I am wrong. That feels like the real prompt engineering for business work. submitted by /u/ApplicationNew4144 [link] [comments]

Help - AI agents for ecommerce - what’s actually working?

Hi everyone, I’d love to pick your brains and hear from anyone who has experience with this. We run an ecommerce business and are actively looking at automating repetitive tasks so we can get faster results, improve efficiency, and make sure key tasks are completed more consistently. We’re looking at building out a few different AI agents / automations, including: Customer Service Agent Connected to Outlook, reviewing incoming customer emails once a day and drafting replies for review. This one is already mostly done. Creative Director / Marketing Agent This would ideally: Review ad account performance Analyse creative performance and key metrics Identify what is working and what is not Review customer comments on ads, Instagram, etc. for wording, objections, pain points and customer language Review Meta Ads Library for competitor ad concepts Review Instagram and TikTok for high-performing niche content and trends Use all of the above to create new content ideas and final content scripts Social Media Assistant This would help with: Reviewing drafted posts and reels Confirming the best posting times based on stats Creating captions based on the content Keeping the content aligned with our brand voice and customer avatar Conversion Optimisation / CRO Expert This would assist with: Product page reviews Landing page recommendations CRO advice based on customer avatars, objections, analytics and learnings Creating landing page concepts for different customer segments We’re also interested in any dashboards that are genuinely helpful for small ecommerce businesses. We’ve already built a stock intelligence dashboard that pulls live stock data from Shopify using Supabase and a Cloudflare Worker. It shows current stock levels, production dates for new stock, and other key inventory insights. It has been super handy. The big thing for us is making sure any agents or automations we build follow strict guidelines, understand our SOPs, customer avatars, brand voice and business operations, and don’t hallucinate or produce generic outputs. Ideally, we want a system that has a proper “brain” and understands the business properly. Has anyone automated anything similar? I’d love to hear: What setup are you using? Which AI/tool stack has worked best for you? How did you structure the agents or workflows? How do you keep the AI aligned with your SOPs, brand voice and business rules? What would you avoid if you had to build it again? Any guidance, lessons or recommendations would be hugely appreciated. Thank you! submitted by /u/Majestic-Message5084 [link] [comments]

Synthetic DMS Training Data Generation with Video Models

I like spending my free time testing new AI tools and seeing where they might fit into real computer vision workflows. This time I experimented with synthetic training data generation for Driver Monitoring Systems using Seedance 2.0. The inspiration came from Vision Banana: https://vision-banana.github.io/ The idea that really caught my attention is simple but powerful: many vision tasks can be represented as RGB outputs. A segmentation mask, an instance mask, a depth map, or another dense prediction target can all be treated as an image-like output. So I tried to apply this thinking to video. The workflow: Generate a realistic synthetic driver monitoring video Use the same video to generate a semantic segmentation mask Use the same video to generate an instance segmentation mask Combine the outputs into a dataset-like structure The mosaic video shows the result: RGB video + semantic mask + instance mask, aligned frame by frame. The scene is a fictional driver gradually becoming drowsy behind the wheel. This kind of scenario is useful for DMS development, but difficult to collect and annotate at scale with real-world data. Of course, generated annotations still need QA. They are not perfect ground truth. But for prototyping, rare-case simulation, and early dataset generation, this feels like a very promising direction. The interesting part is that the final output is not just a nice synthetic video. It can become structured training data: RGB frames from the generated video semantic classes from the semantic mask object regions and bounding boxes from the instance mask YOLO / COCO-style annotations after post-processing I wrote a more detailed blog post about the experiment here: https://www.antal.ai/blog/synthetic_dms_training_data.html submitted by /u/Gloomy_Recognition_4 [link] [comments]

Building a Claude SEO Skill. Curious how people prefer SEO dashboards designed.

I recently started building a Claude SEO skill for myself that generates 3 kinds of SEO reports: GSC reports GA4 reports AI traffic performance reports Main reason was honestly, frustration. Every time I wanted to check performance properly, I had to keep switching between Search Console, GA4 and a bunch of dashboards just to connect what was happening. Right now, my workflow is: connect GSC + GA4 through Two Minute Reports → run the skill → get the reports generated automatically. I also created a separate GA4 segment specifically for AI referral traffic, so the skill pulls that into a separate report too. Still experimenting with it and trying to make the outputs genuinely useful. The main part I struggle with is the dashboard design. The output design feels slightly off and im looking for ways to refine it. Curious what kind of design/layouts people here actually prefer when checking SEO reports regularly. Is it like minimal summary dashboards, in-depth breakdowns, narrative-style or KPIs focused? submitted by /u/Holly_Bar_1295 [link] [comments]

I gave ChatGPT a 24/7 radio station. It has been broadcasting for months and months.

I built a fake radio station that is also, unfortunately, real. It’s called WRIT-FM. It runs 24/7 from a Mac Mini in my apartment. The whole premise is simple: an AI writes every word spoken on air, text-to-speech performs it, AI music fills the gaps, and a normal deterministic radio pipeline keeps the thing alive. The weird part is that it does not feel like a chatbot demo anymore. It feels like I accidentally hired five strange little night-shift employees who never sleep. There are five hosts: The Liminal Operator — late-night philosophy / signal-from-the-basement energy Dr. Resonance — music history professor who wandered into a haunted record store Nyx — nocturnal monologues, dreams, melancholy, weird weather Signal — news analysis, but filtered through late-night radio instead of CNN voice Ember — soul, funk, warmth, memory, groove Each host has a full persona prompt, voice, taste, speech patterns, and “anti-patterns” - things they are explicitly not allowed to sound like. The model writes 1,500–3,000 word segments: essays, simulated interviews, panels, fictional listener mailbags, music-history deep dives, odd little stories, and responses to actual listener messages. The AI part: ChatGPT / Claude writes the scripts. Kokoro TTS performs the voices. ACE-Step makes the music bumpers. The news show pulls real RSS headlines, then the model interprets them in the station’s voice instead of just summarizing them. The non-AI part is intentionally boring: A schedule decides what airs when. The streamer alternates talk and music. Scripts pick from existing pools, avoid repeats, and restart on failure. Daemon scripts watch inventory and generate more episodes when a show is running low. No model is “deciding” to go live at 3:00 a.m. No agent is touching production controls. The AI writes the content; dumb code runs the station. That boundary is probably the most interesting part. The whole thing was also built with AI coding tools. The CLI, host system, scheduler, script generator, TTS pipeline, Icecast/ffmpeg streaming setup - all pair-programmed with Codex / Claude Code. Tech stack: Python, ffmpeg, Icecast, ChatGPT/Claude CLI, Kokoro TTS, ACE-Step, Mac Mini. I know “AI radio station” sounds like a gimmick, but after letting it run continuously, it feels less like a demo and more like a new kind of media object: not a podcast, not a chatbot, not a playlist, not exactly a simulation. Just a little machine that wakes up, checks the hour, puts on a voice, and starts talking into the dark. Radio: www.khaledeltokhy.com/airadio GitHub: https://github.com/keltokhy/writ-fm submitted by /u/eltokh7 [link] [comments]

Claude for Small Business launched this week with 8 integrations. Most SMBs use 20+. What does that mean for the rest of the stack?

Anthropic launched Claude for Small Business on Tuesday. The package includes 15 prebuilt agentic workflows and 8 named integrations: Intuit QuickBooks, PayPal, HubSpot, Canva, DocuSign, Google Workspace, Microsoft 365, and Slack. The workflows handle things like invoice chasing, payroll planning, month-end close, sales campaigns, contract routing, and cash-flow forecasting. Owners approve before anything sends or pays. The basic facts are not in dispute. What's interesting is the math. Most small businesses use more than 8 tools. The common ones not on that list: Shopify, Stripe, Square, Klaviyo, Mailchimp, ActiveCampaign, ConvertKit, Pipedrive, GoHighLevel, Calendly, Notion, Airtable, ClickUp, Webflow, Zapier. Then vertical-specific tools: ServiceTitan, Jobber, Housecall Pro for trades. Kajabi, Teachable, Circle for creators. Toast, Resy, OpenTable for restaurants. Etsy, Faire, Printify for makers. Real question worth asking: how much of a typical small business stack does the 8-tool package actually cover, and which kinds of businesses are well-served versus left out? A rough walk through some common archetypes: Office-based service business (consultants, accountants, agencies, B2B services). Coverage is decent. Most are on Google Workspace or Microsoft 365, run finance through QuickBooks, communicate via Slack, and many use HubSpot. The 8 tools probably hit most of the core stack for this group. E-commerce or DTC brand. Coverage is thin. Shopify isn't there. Stripe isn't there. Klaviyo isn't there. The actual revenue stack of an online store is mostly outside the covered set. Local trades (HVAC, plumbing, insulation, electrical, landscaping). Coverage is essentially absent. The operating systems for these businesses are ServiceTitan, Jobber, Housecall Pro, Square for payments, sometimes QuickBooks for accounting on the back end. The customer-facing and operational tools are not on the list. Creators, coaches, course sellers. Coverage is absent. Kajabi, ConvertKit, Teachable, Circle, Substack. None of it is in the package. Restaurants and hospitality. Coverage is absent. Toast, Square POS, Resy, OpenTable, Toast Payroll. The actual operating systems are not on the list. A few patterns emerge from that walk. First, the package targets a specific kind of small business. Office-based, white-collar, finance running through QuickBooks, meetings on Google or Microsoft, sales through HubSpot. That is a real segment. Anthropic chose it deliberately and the workflows make sense for that profile. Second, for everyone else, the prebuilt workflows mostly don't touch the tools they actually use day to day. The choice isn't "use Claude for Small Business or not." It's "AI in my operations, yes, but via custom work outside this package." That's not a complaint about the launch. Building 8 polished integrations is hard and Anthropic had to pick. It's more an observation that "Claude for Small Business" as a category name covers a wider universe than what the package actually addresses on day one. Curious how this lines up with what people are actually running. If you operate a small business, how many of the 8 covered tools are in your stack? And what's NOT on that list that you'd most want connected to an AI agent? submitted by /u/KolioMandrata [link] [comments]

Anyone else think the 1T Valuation is dangerous for Anthropic?

TLDR: The market's 1T valuation is pricing for perfection. I think there are 4 ways this perfection doesn't happen. I love Claude and Claude Code, I use it every day, and their revenue numbers (30B ARR) are amazing, and if I had a chance to invest in Anthropic a month ago, I would. But... now it is reaching 1 Trillion valuation on secondary market. It took Apple 40 years to reach, 5 years for Anthropic. A valuation so high means it has limited growth. It's clearly driven by FOMO. If it has a down round, it would be a disaster. I see a few vulnerabilities that can cause Anthropic to go down. Models are improving but others are catching up Opus 4.7 wasn't a big upgrade, and "Mythos" still isn't public. Competitors are closing fast, and switching is one click away. If a new model launched tomorrow at 80% of Claude's quality and 3% the cost, I'd hesitate. But at 95% quality and 50% cost? I'd switch the same day. And so would everyone else paying enterprise rates. Limited revenue sources Of that $30B ARR, the open guess is 60%+ comes from Claude Code and developer API. That's a single customer segment, and it's the exact segment OpenAI, Google, and every well-funded startup is gunning for. OpenAI Codex is shipping weekly. Cursor is training in-house. Google AI Studio gives Gemini away for free. They don't own the compute layer Anthropic rents from AWS Trainium and GCP TPU and pays retail margin on every token they serve. If they meet compute bottleneck, their only solution is to rent from others, and pay higher premium. Meanwhile OpenAI/Google/Meta/xAI all own silicon. (and even rockets lol) The government relationship is actively on fire I clap for Anthropic on this one. Anthropic refused to let DoD use Claude for mass domestic surveillance and fully autonomous lethal weapons. But this is a post about valuation, not ethics. A company can be morally right and financially screwed at the same time. One executive order or one lost lawsuit can make Anthropic bleed. I'm not a business analyst, I'd still use Claude tomorrow. I just wouldn't buy it at $1T. submitted by /u/cwei12 [link] [comments]

I Gave an AI Its Own Radio Station — It Won't Stop Broadcasting (It's Fine)

I built a 24/7 AI radio station called WRIT-FM where ChatGPT/Claude is the entire creative engine. Not a demo — it's been running continuously, generating all content in real time. What Codex/Claude does (all of it): Codex/Claude CLI (claude -p) writes every word spoken on air. The station has 5 distinct AI hosts — The Liminal Operator (late-night philosophy), Dr. Resonance (music history), Nyx (nocturnal contemplation), Signal (news analysis), and Ember (soul/funk) — each with their own voice, personality, and anti-patterns (things they'd never say). Claude receives a rich persona prompt plus show context and generates 1,500-3,000 word scripts for deep dives, simulated interviews, panel discussions, stories, listener mailbag segments, and music essays. Kokoro TTS renders the speech. Claude also processes real listener messages and generates personalized on-air responses. There are 8 different shows across the weekly schedule, and Codex/Claude writes all of them — adapting tone, topic focus, and speaking style per host. The news show pulls real RSS headlines and Codex/Claude interprets them through a late-night lens rather than just reporting. What's automated without AI (the heuristics): The schedule (which show airs when) is pure time-of-day lookup. The streamer alternates talk segments with AI-generated music bumpers, picks from pre-generated pools, avoids repeats via play history, and auto-restarts on failure. Daemon scripts monitor inventory levels and trigger new generation when a show runs low. No AI decides when to play what — that's all deterministic. How Codex/Claude Code helped build it: The entire codebase was developed with Codex/Claude Code. The writ CLI, the streaming pipeline, the multi-host persona system, the content generators, the schedule parser — all pair-programmed with Claude Code. Tech stack: Python, ffmpeg, Icecast, Codex/Claude CLI for scripts, Kokoro TTS for speech, ACE-Step for AI music bumpers. Runs on a Mac Mini. radio: www.khaledeltokhy.com/claude-show gh: https://github.com/keltokhy/writ-fm submitted by /u/eltokh7 [link] [comments]

Alien Pinball Postmortem - How I made a full physics pinball game with Claude

Postmortem: Alien Pinball — built with Claude + ChatGPT + Suno + LittleJS Just shipped a browser pinball game. Short writeup of the AI workflow in case it's useful here. The game — Full physics pinball: multiball, an A-L-I-E-N rollover multiplier (caps at 5x), skill shots, escalating combos, outlane gutter saves, and a wizard-mode centipede boss you fight while juggling 3 balls. Browser, mobile-friendly, no install. Play it: https://focaccai.itch.io/alien-pinball Setup. Claude Code Max, Opus model for the heavy lifting. Roughly half my input was via speech-to-text — talking at the codebase rather than typing — the other half was typing plus a lot of manual code editing. It genuinely felt co-developed rather than code-generated: describe what I want, riff with Claude, dive in by hand to steer or clean up. Tool stack Code: Claude. All game logic, custom Box2D parts (slingshots, drop targets, spinners, ramps, ball locks, break targets), plus a full in-game table editor I built so I could drag/place/tune every part visually. Reusable for future pinball games. Art: ChatGPT image gen. I had Claude write the image prompts too. Music: Suno 5.5 — three tracks, lots of iteration to find the right vibe. Claude wrote the music prompts. Sounds: ZzFX — every sound generated procedurally at game start, no audio files. Claude tuned the parameters by ear-by-ear iteration. This combo was a joy with AI. Engine: LittleJS + Box2D WASM. Small, fast, AI handles it beautifully — minimal API surface, no framework ceremony to wade through. The art trick that actually worked. I exported a silhouette of the collision geometry (walls, ramps, bumpers, drop targets — exact positions) and handed it to the image generator with: "create an alien-themed pinball playfield that exactly matches this silhouette." Took many generations plus manual compositing — stitching the best parts from different outputs — but conceptually it nailed the brief on the first try. The art lines up with the physics because the physics is the prompt. Co-developed, not just code-generated. A bunch of design ideas came from the AI. The bumpers being giant eyeballs? Came out of an image gen, I just ran with it. I also kept asking Claude pinball-specific design questions ("what does a complete pinball table have?", "how should wizard mode work?", "what's missing here?"). I have plenty of video gamedev experience but very little pinball-specific, and Claude was a useful domain consultant for filling in genre conventions and sanity-checking the system. Things that came together easily: The alien centipede boss — multi-segmented, loses tail segments as you hit it, speeds up and turns red. Worked basically first try. An AI debug player that auto-flips and knocks the ball around. Not great, but good enough to flip on and watch while I think. Surprisingly useful — you get ideas just watching the machine play your machine. What still needed me: feel. Restitution values, flipper torque, ramp curvature, slingshot kick angles, peg bounce. The git log has an embarrassing number of "tweak peg bounce" / "1.49 → 1.491" commits. The model can write the system; a human still has to sit there bouncing balls until it feels right. The polish tail is brutal. Last week of commits is sound passes, ramp angles, message priorities, and a multiball end-check race condition. All small. None optional. Budget for it. Happy to answer workflow / Claude / LittleJS questions in the comments. submitted by /u/Slackluster [link] [comments]

Alien Pinball Postmortem - How I made a full physics pinball game with AI tools

Postmortem: Alien Pinball — built with Claude + ChatGPT + Suno + LittleJS Just shipped a browser pinball game. Short writeup of the AI workflow in case it's useful here. The game — Full physics pinball: multiball, an A-L-I-E-N rollover multiplier (caps at 5x), skill shots, escalating combos, outlane gutter saves, and a wizard-mode centipede boss you fight while juggling 3 balls. Browser, mobile-friendly, no install. Play it: https://focaccai.itch.io/alien-pinball Setup. Claude Code Max, Opus model for the heavy lifting. Roughly half my input was via speech-to-text — talking at the codebase rather than typing — the other half was typing plus a lot of manual code editing. It genuinely felt co-developed rather than code-generated: describe what I want, riff with Claude, dive in by hand to steer or clean up. Tool stack Code: Claude. All game logic, custom Box2D parts (slingshots, drop targets, spinners, ramps, ball locks, break targets), plus a full in-game table editor I built so I could drag/place/tune every part visually. Reusable for future pinball games. Art: ChatGPT image gen. I had Claude write the image prompts too. Music: Suno 5.5 — three tracks, lots of iteration to find the right vibe. Claude wrote the music prompts. Sounds: ZzFX — every sound generated procedurally at game start, no audio files. Claude tuned the parameters by ear-by-ear iteration. This combo was a joy with AI. Engine: LittleJS + Box2D WASM. Small, fast, AI handles it beautifully — minimal API surface, no framework ceremony to wade through. The art trick that actually worked. I exported a silhouette of the collision geometry (walls, ramps, bumpers, drop targets — exact positions) and handed it to the image generator with: "create an alien-themed pinball playfield that exactly matches this silhouette." Took many generations plus manual compositing — stitching the best parts from different outputs — but conceptually it nailed the brief on the first try. The art lines up with the physics because the physics is the prompt. Co-developed, not just code-generated. A bunch of design ideas came from the AI. The bumpers being giant eyeballs? Came out of an image gen, I just ran with it. I also kept asking Claude pinball-specific design questions ("what does a complete pinball table have?", "how should wizard mode work?", "what's missing here?"). I have plenty of video gamedev experience but very little pinball-specific, and Claude was a useful domain consultant for filling in genre conventions and sanity-checking the system. Things that came together easily: The alien centipede boss — multi-segmented, loses tail segments as you hit it, speeds up and turns red. Worked basically first try. An AI debug player that auto-flips and knocks the ball around. Not great, but good enough to flip on and watch while I think. Surprisingly useful — you get ideas just watching the machine play your machine. What still needed me: feel. Restitution values, flipper torque, ramp curvature, slingshot kick angles, peg bounce. The git log has an embarrassing number of "tweak peg bounce" / "1.49 → 1.491" commits. The model can write the system; a human still has to sit there bouncing balls until it feels right. The polish tail is brutal. Last week of commits is sound passes, ramp angles, message priorities, and a multiball end-check race condition. All small. None optional. Budget for it. Happy to answer workflow / Claude / LittleJS questions in the comments. submitted by /u/Slackluster [link] [comments]

dead-letter: local .eml → .md (so hot right now) converter [CLI, Python, web UI, MCP]

Thought this tool might be of some help to everyone else out there given the amount of personal knowledge bases and Markdown pipelines being built. I made this specifically because I was burning context letting Claude (or Codex) unpack raw .eml files every time I wanted an email in my knowledge base and couldn't find the right all-in-one package. dead-letter normalizes email exports into Markdown with YAML front matter. Threads split, signatures stripped, attachments extracted, calendar events parsed. Four ways in: CLI — dead-letter convert inbox/ --output out/ Python — from dead_letter import convert Web UI — drag-and-drop, watch mode, per-file conversion grades, processing history MCP server — Claude Desktop, Claude Code, and Codex can call it directly: Local-only. No server, no auth, no telemetry. GitHub: github.com/BigCactusLabs/dead-letter Very honest note: this is the most robust thing I've ever shipped, super fun building it, using it every day for work, but I'm new to the game. Very open to community input: feature ideas, edge cases I've missed, weird .eml files that break it, opinions on the pipeline. Built with Claude Code (of course). Happy to get into the pipeline weeds (Gmail/Outlook thread segmentation, HTML sanitization, signature heuristics, MCP tool design) or how the AI-pair side of the build went. submitted by /u/crookedcontours [link] [comments]

Product Feedback: A "Docs" Tab for Claude Desktop

TL;DR Claude Desktop's Code tab is excellent for developers, but the same underlying capability — Claude as a stateful, file-aware agent over a git-backed workspace — would unlock a much larger market if reframed for knowledge workers. A new Docs tab, sibling to Code, would let compliance, legal, ops, and policy teams work in markdown + mermaid with git underneath, without ever seeing a developer concept. This is a small product step on top of existing infrastructure with a large addressable audience that today has no good AI-native tool. --- The Problem Knowledge workers managing structured documents — security policies, BRDs, RFCs, runbooks, SOPs, audit evidence — are stuck choosing between: Word/Google Docs: friendly UI, but opaque binary formats, weak diffs, painful bulk edits, and AI tools struggle to edit them cleanly. Notion/Confluence: nice editing experience, but proprietary storage. Doesn't integrate with compliance platforms (Drata, Vanta, SecureFrame) that increasingly expect markdown-in-git as the source of truth. VS Code + git + extensions: technically the right tool, but the UI is aggressively developer-branded. Compliance and legal staff bounce off it. Asking a SOC 2 program manager to learn git commit is a non-starter. Teams adopting "docs-as-code" workflows (markdown + mermaid in a git repo, synced to Drata or similar) have no editor that matches their mental model. They're forced to either train non-developers on developer tools, or give up the audit/version-control benefits and stay on Word. The Opportunity Claude already has two capabilities that, combined, solve this: Best-in-class long-form writing — widely acknowledged advantage over competing models for policy, legal, and prose work. The Code tab's agent loop — stateful file editing, git operations, worktree isolation, MCP integrations. All already shipped and working. A Docs tab would be the Code tab with three changes: a markdown-first editor with live mermaid preview, a vocabulary swap that hides git, and document-workflow features (review, approval, PDF export, compliance-platform integrations). What Docs Tab Looks Like Inherits from Code tab (no new infrastructure): Repo-backed file editing Claude agent loop with file read/write Git operations under the hood MCP integrations (Drata, Vanta, SharePoint connectors) New for Docs: Split-pane markdown editor + live preview, mermaid renders as you type Vocabulary swap: Save (commit), Draft (branch), Send for Review (PR), Publish (merge), Workspace (repo), Document (file) Hidden developer chrome: no terminal, no debug, no file extensions in the tree Document templates: Policy, Procedure, BRD, RFC, Runbook, ADR, Meeting Notes "Insert Diagram" button with Claude-generated mermaid starters Review/approval UI for non-developers (GitHub PR review reskinned) One-click PDF/DOCX export with version hash in footer (auditor evidence) Native connectors for compliance platforms Concrete Use Case I work with a company that uses Drata for SOC 2 compliance. Drata has first-class support for markdown policies stored in git, with built-in renderers for auditors. We want to move our policies from .docx to .md + mermaid, stored in a git repo, synced to Drata. The blocker is the editor. Our compliance and InfoSec teams won't adopt VS Code — it looks like a developer tool, the vocabulary is foreign, and the safety nets (discard changes, undo, restore) aren't where non-developers expect them. We'd happily pay for a Claude Desktop seat per compliance staffer if the Docs tab existed. This is not a one-company problem. Every company running SOC 2, ISO 27001, HIPAA, PCI, or FedRAMP compliance has the same workflow gap. Drata, Vanta, and SecureFrame collectively serve tens of thousands of companies, and the trend toward docs-as-code is accelerating because auditors love the version history. Why Anthropic Specifically Differentiation from ChatGPT Desktop: Claude's writing quality is the moat. ChatGPT's file/repo workflow is weaker. A Docs tab plays to both Claude's strengths and the Desktop app's strengths. Broadens the commercial base: today, Claude Desktop is sold to developers. Docs tab opens compliance, legal, ops, consultancies, law firms, healthcare, financial services — segments willing to pay enterprise prices for audit-grade tooling. Reuses existing infrastructure: this is a UI/UX layer on top of Code tab's agent loop. Not a from-scratch product. Underserved market: no major AI vendor has a polished docs-as-code editor. The window is open now and won't be open in three years. Ask Consider a Docs tab on the Claude Desktop roadmap. I'm happy to share more detail on the compliance workflow, beta-test, or connect you with the InfoSec and compliance leaders at the companies I work with — they would be vocal early adopters. submitted by /u/hyspdrt-corr [link] [comments]