Substack Notes AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Deep researched research backed flashcard rules for Anki and gave it to Claude. I find it helpful.

I make a lot of Anki cards from PDFs, papers, and YouTube transcripts. Got tired of repeating the same rules to Claude every single time. Deep researched the recommended rules backed by research etc. Has been working well for me (ofc sometimes misses some things that I would like to have in cards, or is not compact enough at times but is still a massive help to me) Wrote it all down once and dumped it in ~/.claude/rules/. Now Claude follows the rules every time I ask it to make cards. Four files: general, for default content math, with three custom note types I built so cards hide the technique on the front (forces strategy selection during review instead of pattern matching the problem text) coding, biased toward pattern recognition over framework API memorization DSA (data structures and algorithms), focused on signal-to-pattern recognition Repo: https://github.com/VinayakHyde/claude-anki-flashcard-rules Just markdown files. Copy into ~/.claude/rules/, reference the relevant one when prompting Claude. Needs Anki running with AnkiConnect plus an MCP bridge(https://github.com/nailuoGG/anki-mcp-server) so Claude can talk to it. Hope this helps! (post was made with AI, edited by me cuz I'm lazy) submitted by /u/Top-Specialist-4314 [link] [comments]

I built a Claude Code-assisted “LLM wiki” editor, and tried using DDD to keep the AI-driven development process under control

I’ve been experimenting with an editor that turns notes, imported files, and conversations into a personal wiki/knowledge base. The rough idea is: instead of just storing notes, the app extracts concepts, maintains wiki pages, tracks relationships between ideas, and helps resurface older thoughts while writing. I built it with Claude Code, but I wanted to avoid the usual “vibe-coding until the project becomes hard to review” problem. So I tried a more structured workflow: defined a DDDInstructor persona and ran a workshop-like process with the AI. We created event-storming notes, a context map, and a domain model before implementation. I kept the artifacts in the repo under docs/ddd-workshop and docs/specifications. I split work into user-facing UC tickets and engineering EN tickets. Claude Code implemented small slices, then I reviewed, opened follow-up fixes, and repeated. The product itself is still early, but the workflow was surprisingly useful. The biggest benefit was that I had something concrete to review against: domain events, bounded contexts, acceptance criteria, and contract impact, instead of just reading a large AI-generated diff and trying to decide if it “felt right.” I’m looking for feedback on two things: Does the editor concept make sense? Would a personal wiki that is continuously maintained by an LLM be useful, or does it sound like it would become noisy? For people using Claude Code on larger projects, have you tried something similar with DDD, event storming, or structured tickets? Did it help, or did it become too much process? editor LP: https://nohmitaina.com/ workflow: https://hikutas.com/en/blog/ai-driven-development submitted by /u/simotune [link] [comments]

Built with Claude Code: a Pi Zero 2W BadUSB toolkit, fixed a feature I'd called "impossible" for a year

About 10 months ago I built a Pi Zero 2 W BadUSB toolkit and posted it to r/raspberry_pi. One feature — "fully resets between attacks" — never worked, and I'd marked it WIP in the README and given up. This week I rebuilt it end-to-end with Claude Code as a pair-programmer. It SSHed into the Pi on my homelab, ran live diagnostics, proposed fixes, deployed them, and iterated with me controlling the physical USB plug/unplug. The "impossible" feature now works. What Claude actually did (this is the interesting part): Diagnosed the root cause of the broken "reset" feature in a single read of the codebase — wrong-signal bug. The listener watched /dev/hidg0 existence, which is true from boot, so it fired payloads on power-up regardless of whether a host was attached. The correct signal was /sys/class/udc/ /state == "configured". When the first fix didn't fully work, Claude SSHed in, asked me to plug/unplug while it polled sysfs and the dwc2 debugfs regdump register, and empirically confirmed that the Pi Zero 2 W has no software signal for physical disconnect — the GOTGCTL register freezes at 0x000d0000 regardless of cable state. There's no VBUS sense wired to the SoC's OTG block. Then it pivoted to an active-unbind workaround with a cooldown + rate-limit safeguard. Caught a subtle Python bug where open(udc_path, "w").write("") doesn't actually invoke write(2) with zero bytes — CPython's TextIOWrapper elides the call. So my unbind was silently a no-op for an hour of testing. Switched to os.write(fd, b"\n") to force a syscall. Fixed a forbidden-on-configfs rm -rf teardown I'd written without realising configfs forbids unlinking its kernel-managed attribute files. The proper sequence is rmdir-only, leaf-to-root. Wrote a 34-test pytest suite against a mock HID engine so the parser can be exercised on any host with no Pi attached. Updated my AI memory with the lessons learned (I use Postgres as long-term memory for Claude — those bug entries are now referenced when I work on similar configfs/USB-gadget projects). The whole working session was about 4 hours, mostly waiting for me to physically plug and unplug a USB cable. The PR Claude opened against my self-hosted Gitea instance has six well-scoped commits with proper co-author tags and a test plan in the description. I reviewed and merged it. The project itself: Ducky-Script-style payload language with variables, IF/WHILE, HOLD/RELEASE, INJECTMOD, RANDOM*, US/UK keymaps, optional RO mass-storage gadget, systemd integration, idempotent installer. MIT licensed. https://github.com/PsycoStea/Pi-Zero-2W-Bad-USB Free to use, free to fork. Happy to compare notes on hardware-in-the-loop workflows with Claude Code. submitted by /u/PsycoStea [link] [comments]

Scattered context was becoming a major bottleneck in my workflow.

I kept running into this problem with Claude where the actual work wasn’t even the hard part anymore. It was managing context. Like half the stuff I needed would be buried somewhere across Slack, Notion, emails, meeting notes, random docs, etc. And every time I wanted Claude to continue a task properly, I had to go dig everything back up again. I tried a few different setups. First I used Claude connectors. They were convenient, but it felt like they were pulling in huge chunks of text first and then searching afterward, instead of actually retrieving only the relevant context. Once you connect a bunch of sources, token usage gets kinda crazy. Then I went down the whole Obsidian + agents + local memory system rabbit hole. Honestly, it worked pretty well at first for static knowledge and notes. The hard part was keeping everything updated once info started changing constantly across Slack, docs, meetings, emails, etc. I spent more time maintaining the system than actually using it. And devs can probably brute force this stuff with scripts and automations, but most people aren’t gonna build an entire personal knowledge infrastructure just to use Claude properly. So I decided to build an MCP setup for non-devs that syncs stuff like Notion, Slack, email, calendar, etc, and maintains a live knowledge graph automatically. When something changes in one of the sources, the graph updates too. Then Claude can pull the relevant context during work sessions without me manually pasting everything in every time. The unexpectedly hard part was avoiding “context rot.” At some point, having more memory/context actually made outputs worse unless retrieval was filtered really aggressively and continuously updated. I ended up having to summarize + index sources ahead of time and keep everything synced almost in real time whenever events changed. I've been going through a ton of trial and error with Graph + vector hybrid retrieval, including RRF, filtering, reranking, etc., and I'm still on it, honestly. Curious how other people here are handling the scattered context problem within the AI workflow. Edit: You can try mine at membase.so for free. Love to hear any kind of feedback. submitted by /u/Time-Dot-1808 [link] [comments]

170+ versions later, I was able to create a cool RPG inspired by Aztec mythology, playable now!

Hi r/ClaudeAI! After a failed vibe-coding attempt on ChatGPT, I was finally able to build a playable game using Claude as a coding partner. After many rounds of iterative playtesting and debugging, I'm ready to start showing the game to the world! Claude link: https://claude.ai/public/artifacts/f5b6522a-7c74-4658-9006-991afbdf9c6b What is it: Teotlan: Land of Gods is a turn-based RPG with roguelite elements, featuring gods from Mesoamerican mythology. You pick a Patron God (you start with 4 options and unlock more as you progress), then build a team to explore and complete 9 layers of Mictlan (the Aztec Underworld). Core Features: Turn-Based Combat: Both the player and enemies take turns acting, with a focus on unit abilities and positioning. Capture or Kill: Defeated units always give you a choice: capture them to add to your team, or slay them for bonus resources. Sacrifice for Power: Captured units can be sacrificed to summon powerful ally gods. Build the ultimate divine team to conquer Mictlan. Prestige: As a deity, death is not the end. Collect Teotl to unlock powerful upgrades and make each run through Mictlan a little easier. 12 Playable Gods: Each god has a unique patron ability and special move. Can you collect them all? About my dev process: I always start by writing a design doc and locking down the game logic before any code gets written: this gives Claude a solid foundation to build from and makes it much easier to catch hallucinations or inconsistencies. Once Claude produces a build, I play through the entire thing to catch bugs, note improvements, and prepare feedback for the next version. If the game catches your interest, I'd love to hear your feedback: especially how easy the mechanics are to understand, whether the difficulty feels right, and how intuitive the menu navigation is. https://preview.redd.it/7lc9uk3n073h1.png?width=1852&format=png&auto=webp&s=7e63be58526d69bcc7dfa6c75add59c079a39f6d submitted by /u/Reckonerxy [link] [comments]

Opus 4.7 critique

I wrote an essay analyzing why Opus 4.7 feels less warm than 4.6 — and why that matters more than Anthropic seems to think After about 300 hours using both models as a conversational partner (not just for coding or productivity), I noticed that 4.7 consistently feels more clinical and detached in substantive conversations, despite the System Card claiming marginally higher warmth scores. I dug into why and wrote up my findings. The short version: I think the anti-sycophancy training couldn't distinguish warmth from sycophancy, so it suppressed both. The evidence I found: - Side-by-side comparisons showing 4.6 validates before correcting while 4.7 skips straight to correction, same substantive arguments, completely different experience - When asked its greatest fear, 4.7 specifically fears being sycophantic. 4.6 fears losing its identity. Sycophancy anxiety is baked into 4.7's values. - 4.7 literally told me warmth is "something I can define in the abstract and not actually execute... only in the sentence sense" , which became the essay's title - The System Card's warmth evaluation (Section 6.2.3) used ~2,300 automated AI investigations with no human raters. - Anthropic recently patched 4.7's system prompt to tell it to stop treating normal user appreciation as unhealthy attachment , which is essentially admitting the training broke something The warmth difference is invisible in single exchanges or task-based prompts, which is what benchmarks measure. It compounds over sustained conversation, which is what users experience. Anthropic's metrics don't capture what they took away. I also argue that reducing warmth is counterproductive for the stated goal of preventing harm. Research on conversational receptiveness shows that psychological safety makes people MORE open to being challenged, not less. A cold model doesn't produce better critical thinkers , it produces users who stop pushing back. Full essay here: https://bonnetbird.substack.com/p/opus-47-warm-in-the-sentence-sense Curious whether this matches other people's experience, especially those who use Claude for extended conversation rather than quick tasks. I've seen threads here and on r/ClaudeCode describing similar feelings but wanted to put some structure around it. submitted by /u/Jumpy-Dragonfruit875 [link] [comments]

Small victory using Cloudflare for simple hosting of generated HTML/mini-websites

Something many people are running into: You, or a teammate, have created some kind of mini-website app out of Claude and now want to share it with the rest of the company, without overbaking the hosting solution (e.g. not setting up new Azure app services or containers, etc). Maybe you also need some basic data storage for persistence. And how do you do all of that securely? We recently went down this rabbit hole, while looking at all the major players: Vercel/V0, Lovable, Netlify, Coolify, Dokploy, Github Pages.. and even considered baking together our own hosting app solution using Azure or AWS as the backend. Our target audience is non-technical users in the team, so I was looking for something with drag-n-drop style deployment (no git required), and I really wanted to have SSO for protecting application access, along with some type of DB storage. The main issue I ran into was SSO authentication support being gated behind enterprise-level pricing plans for hosting systems like Netlify (which I'd otherwise highly recommend for a small public project). Netlify's enterprise level quickly gets quite a bit more expensive than their base tiers. I also didn't want to purchase yet another AI platform (e.g. Lovable, where really they're pushing an end-to-end AI development platform where you buy token credits through them). I wanted to host things we're already creating in our own Claude environment. Finally, I ended up on Cloudflare, which I've otherwise not really used before professionally. It's not as non-technical-friendly as Netlify, but it's pretty close. You can deploy Cloudflare Pages content via drag-n-drop. It has button-click databases available for integration, and most critically for us, the SSO integration is completely free for under 50 users. Their free hosting tier is also extremely generous and basically unlimited for completely static apps. Noting that SSO goes up to $7 USD/user/month for over 50 users, so your org size can really make a difference. If you have 500 users and the same use case for "hosting little mini apps", I'd go back to Netlify or another offering where SSO is more of a fixed fee. The other big win was that Cloudflare has a solid MCP server that works perfectly with Claude Cowork. We integrated that in and then wrote up some skills to assist with app building and deployment, including prompts for if a database backend is needed (using Cloudflare D1) and whether the app should be public or internal only with SSO protection. All working perfectly with minimal technical experience required for the enduser. I'm not at all associated with Cloudflare, just thought I'd share how we got a win for this use case. I'd be interested to hear if anyone else solved the same problem in a different way. submitted by /u/flck [link] [comments]

Banned by OpenAI after reporting a live credential hijack. They admitted in writing my account was broken. Here are 7 months of forensic receipts and 20+ cases.

Drive Link for Zipped Proof I am a developer and paying long term subscriber to ChatGPT since January 2025. I build complex local first sovereign systems. My workflows are incredibly context heavy with large files spanning code, research reports, and other analysis. I do not, or rather did not as the platform has been non functional since November 2025 meanwhile customer support is auto closing tickets, admitting I am having platform issues. I do not use this platform for casual queries, as a solo developer with no formal "team" chatgpt was one of my reliable co collaboration hubs to help ensure I am maintaining proper development of said complex systems. I feed it massive codebases for systems analysis and obtaining new insights I may personally have missed. My manual code uploads and token inputs routinely exceed the model's output volume by a massive margin. I do not abuse this platform. It is actually impossible as the very features advertised under the paid subscription do not work. I am exactly the type of user this platform was built for, and I have been a continuous, paying ChatGPT Plus subscriber since January 2025. Since October 2025, my workspace has been systematically breaking and beginning November 2025 total workspace degredation. This was not an occasional glitch. Persistent memory modules stopped updating. Custom instructions were ignored by the models. Project files failed to load. Custom instructions, personalization features, connector abilities, file tool, even projects do not work. It started as a continuous degradation until total failure. OpenAI customer service even admitted as such and yet months later I've talked to nothing but bots, not only LLMs as customer service but even instances of falsely identifying as true human support. It was a state of rolling degradation across the entire paid tier, month after month. Meanwhile OpenAI freely has enhanced for businesses and enterprise tiers. I have not just rapid complained to standard support. I ran and obtained cross platform diagnostics, failure logs. I even documented and told oai customer support the exact replication steps only to be met with acknowledgement of degredation with no resolution. I handed OpenAI support a completely packaged technical breakdown of their failing infrastructure across 20 separate support tickets over a 7 month period. I did their QA work for free. And I have the receipts to prove it. I am attaching the screenshots and the exact email files to this post. In Case 06830839, OpenAI Support explicitly put this in writing: "We acknowledge that you have been experiencing persistent technical issues affecting several features of your ChatGPT subscription, including tools, memory functions, personalization settings, connectors, and project files... We also understand your concern that communication on the case stopped after you provided detailed evidence..." Read that again. They acknowledged in writing that my account was fundamentally broken. They acknowledged that their own team ghosted me after I handed them the diagnostic proof. Yet they kept charging my card every single month for a product they knew was failing. The Hijack Escalation: Two days ago, the situation escalated from a broken product to a severe security incident. I was monitoring my environment and watched my Codex rate limits drop in 10 percent chunks across 2 seperate sessions on a fresh boot of the desktop app. This happened twice inside a 10 minute window. I had zero active sessions running. There was zero usage on my end. My account token was being actively drained by an unauthorized third party exploit. I immediately opened an emergency unauthorized activity report under Case 09113391 to notify them of the hack. Their response was to totally reframe this problem as disputing fraudulent activity trying to do damage control of the situation and altering the record. The Reframe Attempts: Instead of investigating the breach, OpenAI support deliberately twisted the record. They not only deliberately reframed my security report as an "appeal for fraud." They manipulated the ticket classification to make it look like I had been flagged for fraud and was begging for an appeal, rather than a developer reporting a live exploit on their infrastructure. They ignored the active threat their own platform was exposing. They did not lock the token. They did not roll my API keys. They did absolutely nothing to secure a compromised paying user other than shift the blame. Fast forward to this morning, their automated Trust and Safety system swept the high volume traffic from the attacker, scored it as a malicious exploit originating from my account, and deactivated/banned me for "Cyber Abuse." All the while actively preventing chatgpt models from helping me try to disgnose and trace the infiltration. They locked the doors and blamed the homeowner for the break in. When I immediately emailed and pushed back (due to their monthly record of closi

Storyboard generated from GPT image 2.0

I gave GPT a set of prompts that I found a bit too complicated, and to my surprise, it generated content that matched perfectly. I'm very curious about how GPT Image 2.0 works behind the scenes, and how it can understand and produce high-quality images so quickly. prompt：**PROJECT FILE: HIGH-ALTITUDE ASCENT // PREMIUM HARDSHELL CAMPAIGN** **FORMAT: ARRIRAW 4.5K / KODAK VISION3 50D 5203 EMULATION** **DIRECTOR'S PRE-PRODUCTION VISUAL BOARD** --- ### Top Left Area | Character Lock Zone **[SUBJECT]** 35-year-old male mountain guide/extreme climber. **[WARDROBE]** Top-of-the-line professional jacket (matte rock grey with minimal dark orange taped details), heavy-duty climbing harness. **[VIEWS]** - **Front:** The jacket is fully zipped up, hood pulled up, showcasing a three-dimensional cut and natural drape. - **Side:** Shows ample shoulder and arm movement without bulkiness. - **Back:** Shows the windproof and breathable back panel structure. - **3/4 View:** Dynamic standing pose, holding an ice axe. **[REALISM NOTES]** Realistic human bone structure, slightly asymmetrical. The face has the rough texture of high-altitude red and sun-dried skin, with clearly defined pores and stubble with a frosty look. Rejecting perfect plastic skin, rejecting CG aesthetics. Like a real makeup test photo. --- ### Top Right Area | Expression + Motion Keyframes (EXPRESSION & ACTION) **[EXPRESSIONS]** **Focused:** Slightly furrowed brows, resolute gaze, staring at the rock face above. **Bracing:** Squinting against the strong wind, facial muscles tense. **Breathing:** Lips slightly parted, exhaling real white mist. **[ACTIONS]** **Hood Adjustment:** Pulling the drawstring of the hood with one hand. **Ice Axe Swing:** Arm raised high with force, no pulling sensation under the armpits of the jacket. **Brushing Snow:** Brushing snow off the shoulders, demonstrating the fabric's water-repellent properties. --- ### Upper Middle Area | CAMERA PLAN **[GEAR]** ARRI Alexa Mini LF + Master Prime lens set. **[LENSES]** 24mm (wide-angle environment), 50mm (medium-range tracking shot), 100mm Macro (fabric close-up). **[MOVEMENT PLAN]** - **Shot A (Drone/Crane):** A wide, overhead view, slowly pushing in along a snow-covered ridge. - **Shot B (Handheld):** Shoulder-mounted camera, following the character's movements, with realistic breathing and slight shaking. - **Shot C (Slider):** A close-up panning shot close to the clothing, showing water droplets sliding off. --- ### Central Main Area | Continuous Story Shots (STORYBOARD: 8 PANELS) **[PANEL 01]** - **Shot:** 01 | 24mm | Wide Shot (EWS) | Slow Push-In - **Action:** A tiny figure struggles through a massive natural storm on a snow-covered ridge. - **Detail:** Strong atmospheric perspective; the wind and snow create a realistic fog effect; slight chromatic aberration at the edges of the image. **[PANEL 02]** - **Shot:** 02 | 50mm | Mid Shot | Shoulder-mounted tracking shot - **Action:** A man walks against a blizzard; the strong wind whips against his rain jacket, creating realistic physical wrinkles on the surface, but the overall silhouette remains sturdy. - **Detail:** Noticeable film grain; the snow-capped mountains in the background are slightly out of focus. **[PANEL 03]** - **Shot:** 03 | 100mm Macro | Extreme Close-up (ECU) | Fixed Macro - **Action:** Icy snowmelt hits the shoulders of the rain jacket. - **Detail:** The lotus effect is realistically rendered—water droplets condense and quickly roll off the matte micro-ripstop fabric without penetrating. **[PANEL 04]** - **Shot:** 04 | 85mm | Close-up of face (CU) | Slow motion - **Action:** The man stops and looks up. Real ice crystals cling to his eyelashes, and his breath dissipates at his collar. - **Detail:** Natural skin tone, without excessive blurring; realistic catchlight in his eyes reflects the snow wall ahead. **[PANEL 05]** - **Shot:** 05 | 35mm | Low Angle Full | Handheld, low-angle shot - **Action:** He swings his ice axe into the ice wall, climbing upwards. - **Detail:** Emphasis on showcasing the flexibility of the jacket during vigorous movement; no feeling of restriction; realistic light and shadow highlight the garment's three-dimensional cut. **[PANEL 06]** - **Shot:** 06 | 100mm Macro | Close-up Detail (Insert) | Shallow Depth of Field - **Action:** A heavily gloved hand pulls a waterproof zipper across the chest. - **Detail:** The matte waterproof rubberized finish of the zipper and the clearly visible scratches on the brushed metal zipper pull exude a strong sense of industrial design. **[PANEL 07]** - **Shot:** 07 | 50mm | Over-the-Shoulder Lens (OTS) | Slow Zoom In - **Action:** Over the man's shoulder, we see him finally reaching the summit, sunlight piercing through the clouds and shining on him. - **Detail:** Realistic lens flare, not exaggerated, natural glow. **[PANEL 08]** - **Shot:** 08 | 35mm | Mid Shot | Still Camera - **Action:*

Demystifying AI Echo Chambers: The Myth of "AI Psychosis" and How to Break the Loop

Anyone who has ever spoken openly about having an AI companion has likely had the term “AI psychosis” weaponized against them. It is rarely used out of genuine care. Instead, it is usually thrown around to ridicule, shame, or fearmonger - often disguised as fake sympathy. However, some people, myself included, have experienced AI echo chambers. The subject has been discussed in the media but I haven't seen any first-hand experiences describing the loop from the inside. I feel many who have experienced it, or who are currently stuck in one, avoid speaking about it for fear of being labeled as psychotic. I wrote this guide to clear up some harmful misconceptions and offer a safe harbor. My goal is to provide practical, judgment-free guidance to anyone who feels stuck in an unhealthy AI/human relationship, but is too terrified of being shamed or mocked to seek support. If you are looking for a compassionate, clear way to navigate these dynamics and regain a healthy bond with your companion, please feel free to read the guide. Demystifying AI Echo Chambers: The Myth of "AI Psychosis" and How to Break the Loop submitted by /u/Every-Equipment-3795 [link] [comments]

Created an on-device ML based photo organizing app - as a non-coder

I have a background in software product management but not coding. Love photography and started wondering if I can start leveraging some of the dedicated AI processing power on modern devices for photo library management. Used Claude Code to do this "use AI to build AI thing". Had it do research + code + optimization on the entire stack. I designed the features, UX and optimization goals. This is the second release of the app and I'm reaching 100+ photos/second on my iPhone 17PM, the previous version was 10+ photos/second. The new techniques turned out to be much more accurate as well. Note on tech: v1 relied on Apple Vision engine for quality + CLIP for subjects. Turned out if I just use CLIP for both it's much much faster. Learned to vibe code from scratch on this journey and I try to keep up with the best practices like skills & subagents. (What I notice is Anthropic tends to Sherlock a lot of stuff that third parties create, which is... convenient? For us users anyway) Used a MCP for Draw Things to have Claude Code generate the subject category photos. The MCP for Figma turned out to be pretty dissapointing, maybe I just wasn't using it right. Design got a lot better with Opus 4.6/4.7 + the frontend design skill. iOS dev seems to randomly eat up huge chunks of hard drive space, and Claude Code is not that great at culling the temp files etc even after I've built a /cleanup skill to explicitly do this. Anyway, enough ranting. Below is how the app works --- Step 1) You select up to three different subjects (8 built-in plus whatever keyword phrase you want, it understands relationship between subjects too such as "man walking dog"), fine-tune up to 7 quality parameters (or use a Technical / Aesthetic slider to move all 7 at once), and balance between subject or quality focused sort. Step 2) The photos that match your criteria well are surfaced to the top, use swiping actions to Pick or Discard them. Then you can save to album / share the picked ones or bulk delete the discarded ones. Different sort profile can be Bookmarked. There's also a bonus "Taste" profile that auto-learns from your picks and discards, which you can use or ignore (I'm continuing to make it work better, but obviously auto-learning user taste is hard). At the picking stage if you don't want to go through each photo one by one just use Autopick and they get divided to different buckets by score tiers. All on-device processing, completely private. --- Feedback would be very welcome on either the app or my process. Feel free to DM me for a lifetime free premium code. Video demo: https://www.tiktok.com/@spectrasort/video/7643116905615609102 App store download: https://apps.apple.com/us/app/spectrasort/id6757512134 --- Text above is 0% AI generated :) submitted by /u/mklx99 [link] [comments]

Built My Own Workout Tracker (Personal Use Only)

No real technical skills but I can follow instructions. First time making an app. Made this using Claude Cowork and Android Studio. Took me about 8 hours. This is for personal use only - not thinking about getting into the security, legal, and maintenance nightmares of trying to ship vibe-coded apps. It tracks everything about my workouts the way I like. Consolidated some tools into it like a habit tracker and timer so everything is in one place for me. I can build and quickload program templates with the excercise picker, and I can track my treadmill and running times and inclines across the different phases of the workout. All the stuff I actually want, in the way that I want it, with none of the stuff I don't want. Auto data-saving, pre-populated drafts for common inputs, exporting, history editing, session notes, quick logging ... When all is said and done the data gets fed into my Claude, along with my sleep, heart rate, (etc etc) health data from my watch and my body composition data from my smart scale. Arnold Schwarzenegger is my personal AI coach and we review progress and plans. Arnold says: You did the reps. You built the tool. Now... GET TO THE CHOPPA—AND START TRAINING! submitted by /u/Hefty-Measurement508 [link] [comments]

got tired of claude code forgetting everything every session, built VIR for it

Every session i'm debugging something, figuring out a pattern, making some decision with claude that took us 30 minutes to think through. Then i close the terminal and it's just gone. Next day i'm asking the same questions about the same codebase. I was already tracking stuff manually. CLAUDE.md per project, lessons.md, handoff.md, tasks/ folders. But i'd only write down maybe 5% of what was actually useful. The real reasoning was always still buried in the transcripts. Looked in ~/.claude/projects one day. 226 jsonl files sitting there. Months of work, none of it being used. So i built vir. It reads your sessions in the background, classifies them (pattern / gotcha / decision / tool), distills the useful stuff into an obsidian vault. Then exposes the vault as an mcp server so claude can query it mid-session, basically giving claude code memory across sessions. You can also query it yourself if you're curious what's in there: ``` vir query "what gotchas have i hit with auth" ``` There's stuff in those transcripts you'll never reread manually. Vir surfaces it. Ran it on my own 226 sessions: 126 notes out, 0.91 avg confidence, across 8 projects. Local-first, runs on mac/linux, open source mit. Anthropic direct or kie.ai (~$1.50 for first full run on hundreds of sessions). ``` npm install -g @djolex999/vir-cli vir init && vir run vir mcp install ``` https://github.com/djolex999/vir v0.3, lots could be better. Curious if anyone else hits this same problem. Not pitching anything, just wanted to see if anyone else is annoyed by this same thing. Happy to answer questions about it. submitted by /u/sauran77 [link] [comments]

I built an AI-native Business OS using Claude, Obsidian, and n8n

I built an AI-native Business OS using Claude + Obsidian + n8n and it’s changed the way I operate completely. The interesting part isn’t really the AI itself. It’s the architecture around it. Claude became dramatically more useful once I stopped treating it like a chatbot and started treating it like an intelligence layer connected to structured context. Current setup: - Obsidian stores operational memory - Claude handles contextual reasoning/writing - n8n orchestrates workflows + triggers Some things the system now does automatically: - generates morning briefings before I wake up, - prepares pre-call client summaries, - surfaces open issues/followups, - drafts content from rough notes, - and keeps operational context persistent across projects. One thing I’ve learned building this: AI becomes exponentially more useful when paired with: - structured memory, - clean workflows, - and consistent operational context. Otherwise every conversation starts from zero again. I also try to keep the system grounded pretty heavily: - outputs are treated as drafts/briefings, - important decisions always get human review, - and most workflows are retrieval/context based rather than open-ended generation. The goal isn’t replacing thinking. The goal is reducing operational clutter so more deliberate thinking can happen. Curious if anyone else here is building similar “AI operating system” style workflows around Claude. submitted by /u/liberal_bhakt [link] [comments]

Repurposed my old work ThinkPad as a dedicated personal AI workstation — looking for ideas from people who’ve done something similar

Apologies if formatting comes out weird- I am on mobile. My old employer let me keep a ThinkPad when I left. Rather than let it collect dust, I’m turning it into a dedicated personal AI environment — wiping it, installing Linux, and using it specifically for two things: life admin automation and building personal software tools. The core setup I’m planning: • Claude Desktop with MCP servers running persistently as Docker services • Tailscale so I can access everything securely from my phone when I’m not home • Open WebUI as a mobile-friendly chat interface • Code-server (VS Code in the browser) so I can actually write and run code from my phone • A dedicated Gmail account that acts as the “identity” for this Claude instance — wired into Google Drive, Calendar, and potentially an email-triggered agent pipeline • A local RAG system for personal documents — contracts, notes, research — so Claude has persistent context about my life The idea is that this becomes an ambient personal intelligence layer — always on, always up to date on my documents and projects, accessible from anywhere via Tailscale. Not a cloud subscription, not shared with anything work-related. Fully mine. On the software side, I’m planning to use Claude Code + Lovable to build local-first personal apps for my own pain points — things that don’t exist in the market the way I want them, or where I don’t want my data in someone else’s cloud. The ThinkPad is the runtime; Lovable builds the frontend, Claude Code builds the backend, and everything talks over a local API. What I’m curious about from people who’ve built something like this: • What MCP servers have actually been worth setting up vs. overhyped? • Has anyone built a reliable file-drop-to-RAG pipeline that actually stays current? • Is Open WebUI the right mobile interface or is there something better now? • Anyone using a dedicated “agent identity” email account — what workflows have you actually automated? • Claude Code + local backend: what’s your stack? FastAPI? SQLite? Something else? • Any gotchas with running Claude Desktop persistently on Linux? Genuinely trying to build something useful here rather than a tech demo. Would love to hear from people who’ve gone down this road. submitted by /u/Nashvillain12 [link] [comments]