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Second AI

Second AI

Second AI

Second AI

Second AI

Second AI

Second AI

Second AI

Second AI

Second AI

Wow so analog clocks are their kryptonite.

I heard several AI engines have issues with reading analog clocks, so I tried. And here we are. submitted by /u/Ejay222 [link] [comments]

The Hybrid Method: how I split tasks between the chat (Claude.ai) and a background agent (Claude Code)

After a month of running this daily, I've settled on what I call the Hybrid Method: keep Claude.ai (the chat) as my only surface, and delegate engineering work in the background to Claude Code. The chat writes the engineering prompt, launches the executor, supervises through the filesystem and git log, and reports back without me ever opening a terminal. The piece I find most useful to share is the **allocation matrix** — which kind of work goes to which engine. Took weeks of measurement to stabilize. **Background agent (Claude Code) handles:** Large refactors across many files Tedious mechanical work (renaming patterns, applying fixes from a list) Anything that needs filesystem + git access without back-and-forth Tasks that take more than ~2 minutes of pure execution **Chat (Claude.ai) handles:** Architecture decisions and tradeoffs Reviewing the agent's diff and discussing the output Sprint planning while the agent runs the current sprint Quick edits where the round-trip to a background process is wasted Anything where the answer needs human reading anyway **The hand-off:** The chat writes a detailed prompt for the background agent (including a fail-fast spec and what to commit at the end). It launches `claude --headless --instruction "..."` as a subprocess via a small MCP bash bridge (~200 lines of Python using Anthropic's MCP SDK; community implementations exist too). Then it polls the git log and a status file every 30–60 seconds while I plan the next thing. When the agent finishes, the chat reads the diff and reports. **Why "hybrid":** The analogy is the hybrid car. Two engines with different load profiles. The chat is electric — instant startup, smooth low-load, great for transitions and decisions. The background agent is combustion — cold-start cost (5–15 seconds while it loads the project's memory file and explores the repo), but sustained throughput once running. They specialize, they hand off, the user never feels the seam. **What changes from running Claude Code alone:** Context-switching cost drops to near-zero — I never leave the chat session Strategic and execution work happen in parallel (the chat plans the next sprint while the current one runs) The chat acts as supervisor — better wired for high-level reasoning than the executor agent which is wired for action **Caveats:** This is the operator pattern Anthropic has documented elsewhere; the specific assembly (Claude.ai web as the chat + an MCP bash bridge + Claude Code as the executor) is what I haven't found written up specifically No sandboxing on personal hardware; if any of this ever runs on someone else's machine, careful sandboxing is non-negotiable The chat saturates beyond ~2 parallel background tasks — past that, the supervision quality drops Curious whether anyone else has converged on something similar, or what variations work for you. submitted by /u/Krycekk [link] [comments]

Organize your medspa compliance reminders effortlessly. Prompt included.

Hello! Are you tired of keeping track of multiple vendors and their compliance items for your medspa? Do you find it challenging to remember when important documents are due or need renewals? This prompt chain helps you efficiently manage vendor compliance reminders. It assists in organizing your vendor list, standardizing the data, setting reminders for upcoming due dates, and generating a clear audit log for your compliance needs. Prompt: VARIABLE DEFINITIONS [MEDSPA_NAME]=Name of the medspa [VENDOR_LIST]=Raw list of vendors and their compliance items [DEFAULT_REMINDER_LEAD]=Number of days before each due date you want automatic reminders (e.g., 30/15/5) ~ You are the compliance coordinator for [MEDSPA_NAME]. Step 1 – Provide the initial data set. 1. List each vendor on a separate line in the following comma-separated order: Vendor Name, Requirement Type (contract / liability insurance / equipment service / other), Effective Date (YYYY-MM-DD), Expiration or Renewal Due Date (YYYY-MM-DD), Proof Document Type (PDF, email thread, invoice, etc.), Internal Owner (name or role) 2. If a field is unknown, type "TBD". 3. End your list with a blank line. Example input line: ABC Laser Co, equipment service, 2023-10-01, 2024-10-01, service invoice, Clinical Director Please enter the list now. ~ You are an expert data normalizer. Step 2 – Standardize and validate entries. 1. Convert the raw [VENDOR_LIST] into a clean table with the following columns exactly: Vendor, Requirement, Effective Date, Due Date, Proof Needed, Owner. 2. Highlight any TBD fields under a "Data Gaps" section beneath the table, listing Vendor and the missing field. 3. Ask the user to supply missing information or confirm the table is correct. Format the table using pipes (|) as column separators. ~ You are a compliance scheduling assistant. Step 3 – Add reminder cadence. 1. Using the confirmed table, add three new columns: First Reminder, Second Reminder, Final Reminder. 2. Calculate each reminder by subtracting the [DEFAULT_REMINDER_LEAD] day values in order (e.g., 30, 15, 5) from the Due Date. 3. Retain original columns so the new table headers are: Vendor | Requirement | Due Date | Proof Needed | Owner | First Reminder | Second Reminder | Final Reminder. 4. If any calculated reminder date is in the past, mark it “SEND NOW”. 5. Output the updated table only, using pipe separators. ~ You are a documentation specialist. Step 4 – Generate the final audit log deliverable. 1. Present a clear title: "[MEDSPA_NAME] Vendor Compliance Reminder Audit Log". 2. Include the reminder table from Step 3. 3. Under the table, list Data Gaps (if any) and required next actions. 4. Provide a one-sentence summary of overall compliance risk level: GREEN (no gaps), YELLOW (some gaps), RED (many gaps or past-due). ~ Review / Refinement Please confirm that the audit log meets all requirements (each vendor’s requirement, due date, proof needed, reminder cadence, owner) and that dates and owners are correct. • Reply "approve" to finish. • Or list any corrections and we will iterate. Make sure you update the variables in the first prompt: [MEDSPA_NAME], [VENDOR_LIST], [DEFAULT_REMINDER_LEAD]. Here is an example of how to use it: [Example: Your medspa name is ‘Healthy Glow’, you have a list of vendors, and want reminders set 30 days, 15 days, and 5 days before due dates.] If you don't want to type each prompt manually, you can run the Agentic Workers, and it will run autonomously in one click. NOTE: this is not required to run the prompt chain Enjoy! submitted by /u/CalendarVarious3992 [link] [comments]

NOML-NOML: hierarchical TD3 + anchor policy for flight control [P]

I built a custom RL algorithm for continuous flight control and open-sourced it. Sharing here in case the structural ideas are useful for anyone doing continuous control where one action axis dominates. I've been training continuous control on a 6-DoF flight sim (pitch/roll/yaw/throttle/brake/fire) and kept hitting the same wall: vanilla TD3 would peak, then collapse into pitch oscillation and never recover. I tried reward shaping for a while before concluding the problem was structural, not in the reward. NOML is what came out of that. Three structural changes on top of a standard TD3 skeleton: Anchor policy — the action is anchor + delta·gate, where the anchor is a fixed safe action (wings level, MIL throttle). The policy literally cannot fully forget how to fly straight; the worst a collapsed policy can do is fall back to the anchor. Hierarchical actor — three MLPs with independent optimizers (pitch → roll → rest), so a roll-side gradient update can't corrupt the pitch head. This is what actually killed the oscillation for me. Mirror learning — left-right symmetry means every transition can be mirrored into a free second sample. 2× data when env steps are the bottleneck. One thing that surprised me and goes against the usual advice: my best results came with exploration noise effectively off. On this task adding Gaussian action noise mostly just shook the stick and hurt. The anchor+gate structure seems to provide enough of the "fall back to safe behavior" role that noise usually plays. Code (Apache 2.0), full writeup, and a test video are here: https://github.com/9138noms/NOML https://www.youtube.com/watch?v=ZNn6wo_PX8Y submitted by /u/9138NOMS [link] [comments]

wedding planner charleston. 4 years business owner. didn't expect claude to be the tool that changed my business this year.

charleston SC. wedding planner. 4 years. 18-22 weddings per year. average wedding budget $48k. team of 3 (me + 2 day-of coordinators). i don't usually post on this sub because i'm not technical. wanted to share because if claude is useful for a wedding planner in south carolina, it's probably useful for more service-business operators than the typical r/ClaudeAI audience. how i actually use claude. client comms. weddings involve emotional decisions. brides text me at 11pm asking about vendor concerns or family drama. before claude i'd respond in the morning and the bride would have been spiraling for 8 hours. now i type my rough response into claude at night, ask it to soften my tone (i'm direct, brides need warmth), and send the response immediately. response time per emotional message: 90 seconds. brides feel heard. nobody spirals overnight. vendor negotiations. emails to florists, caterers, photographers. i tell claude what i need to negotiate (price, change orders, scheduling conflicts) and the vendor relationship context. claude drafts a firm-but-warm version. i edit. send. saves me ~5 hours a week of vendor email i used to dread. timeline writing. each wedding needs a 14-hour day-of timeline. used to take me 6-8 hours per wedding. now claude takes my notes from the venue walkthrough + the couple's prefs + the vendor schedules and produces a draft. i edit. 2 hours instead of 6. proposal writing. when i'm bidding on a new wedding, claude drafts a proposal based on the consultation call. consistent quality. doesn't depend on whether i'm having a good week. emotional decisions, my side. i'm a wedding planner. clients have meltdowns. i absorb a lot. claude is my journal at the end of hard days. i type out what happened, what i'm feeling, what i should do differently next time. claude reflects back. it's not therapy. it's processing. what surprised me. claude works for non-technical service businesses. i'd been told by friends in tech that claude was "for coders." it's not. it's for anyone who writes things and makes decisions. it gives me back hours i didn't know i was losing. wedding planning is emotional labor as much as logistical labor. claude takes the logistical labor down significantly, which means i have more energy for the emotional labor that actually requires me. my brides notice. they don't know about claude. they notice that my responses are quicker, my timelines are more thorough, my emails sound warmer. they refer me to friends at higher rates than they did before. revenue impact (i tracked this carefully): 2024: ~$184k from 19 weddings. 2025: ~$247k from 22 weddings. partly more weddings. partly higher average wedding budget. some of it is claude. i'd guess 30-40% of the improvement is directly attributable to claude saving me time so i could take on better-fit clients. for other service business operators who think AI is "for tech people." it's not. open the app. talk to it about your business this week. report back here in 60 days. submitted by /u/Temporary-Prior7384 [link] [comments]

Coffee, Claude, and Remotion is all you need to make launch videos.

https://reddit.com/link/1tik0qe/video/9bh6ypr3ca2h1/player A few hours, Claude Code + Remotion, 4 black coffees, no design tools, no After Effects, no editor. The whole trick: Remotion is React for video. You write JSX, you get an mp4. Every animation is interpolate(frame, [start, end], [from, to]). That means Claude Code can write the entire video for you — it already knows React, animation is just numbers, and you can iterate the same way you iterate on a landing page. Change a value, re-render, see what happens. That feedback loop is the whole unlock. I described the scenes I wanted, Claude wrote them, I tweaked timing and cut whatever felt slow. 5 small things that made it not look like a dev made it: Crossfade every cut. Don't hard-cut between scenes. Overlap them and blur-fade. Instantly stops feeling like a slideshow. One easing curve everywhere. cubic-bezier(0.22, 1, 0.36, 1) (expo-out) on every animation. Consistency in motion is 80% of "looks designed." Film grain + vignette overlay. Two dumb components on top of everything — SVG noise at 2% opacity, soft dark vignette. Cheapest cinematic upgrade in existence. Layered audio, not one track. Background music low, plus targeted SFX - whoosh only on chapter cuts, typing during the hook, pop on the CTA. Overdoing SFX is the #1 amateur tell. Cut ruthlessly. If a scene doesn't earn its place in 3 seconds, kill it. The first cut is always too long. Stack: Remotion, React, TypeScript, Claude Code, Google Fonts (DM Sans + Crimson Pro), a few SFX from freesound.org, one royalty-free background track. $0 in tools. Bonus meta thing: the video isn't a screen recording of my product. It's a Remotion-built launch video that features a real video output from my product (the Cultured AF deck one). So I used InkMotion to make the demo footage inside the launch video. Probably should've just used InkMotion to make the whole launch video and saved the 4 coffees. Next time. Happy to answer specifics in the comments. submitted by /u/Top_Commission_8567 [link] [comments]

CANTANTE: Optimizing Agentic Systems via Contrastive Credit Attribution [R]

LLM-based multi-agent systems have demonstrated strong performance across complex real-world tasks, such as software engineering, predictive modeling, and retrieval-augmented generation. Yet, automating their configuration remains a structural challenge. Researchers are often forced into manual, trial-and-error prompt tuning, where a change to a single agent shifts the global output in ways that are difficult to trace. The core bottleneck is credit assignment: while the parameters governing agent behavior are local, performance scores are only available at the global system level. This makes optimization fundamentally difficult because we do not inherently know which agents contributed positively or negatively to the outcome. CANTANTE is an attempt to take a different path: treating agent prompts as parameters learned from task rewards rather than tuned by hand. By solving the credit assignment problem, we can move from brittle, hand-crafted agent demos to trustworthy systems that are actually autonomous and useful in practice. CANTANTE's algorithm in short (see second image): Let local optimizers suggest configurations (e.g., prompts). Evaluate different configurations on the same queries, capturing reasoning traces and system scores. Let an attributer compare these rollouts and assign each agent a credit, thereby decomposing the global reward into per-agent update signals. Feed those credits to any local optimizer; for the experiments, we use CAPO, our prompt optimizer from prior work at AutoML 2025. Evaluated against the DSPy-solutions GEPA and MIPROv2 on MBPP (Programming Benchmark), GSM8K (Mathematical Reasoning Benchmark), and HotpotQA (Retrieval Benchmark), CANTANTE: • Achieves the best average rank, • beats the strongest baseline by +18.9 points on MBPP and +12.5 on GSM8K, and • maintains inference time cost compared to unoptimized prompts. 🔗 Link to the paper: https://arxiv.org/abs/2605.13295 💻 Link to the repo: https://github.com/finitearth/cantante If you're researching multi-agent architectures or automated prompt engineering, I'd love to hear what's working (and breaking) for you right now. submitted by /u/finitearth [link] [comments]

Why Claude Code forgets your stack and how to fix it

Karpathy's "Claude 4 Rules" post points out the biggest pain point for Claude Code: every session starts with a blank slate. The model has no memory of the project's stack, the design decisions you made last week, or the dead-ends you already explored. I ran into the same issue on a 87-file codebase (163 122 tokens). Feeding the same files directly to Claude Code cost roughly 163 000 tokens. After adding the engramx Skill Pack (v4.0.0) the token count dropped to 17 722. That's an 89.1 % reduction, or about 6.4 times fewer tokens than reading only the relevant files, and 25, 155 times fewer than scanning the whole repo. The reduction comes from three things. First, engramx builds a bi-temporal knowledge graph from your git history. A git-revert miner automatically captures revert commits during indexing, so you get a curated mistakes corpus without any manual effort. Second, bi-temporal mistakes now fire as PreToolUse hooks on Edit, Write, and Bash actions. The model sees the mistake before it retries, so it can avoid repeating it. Third, engram init installs six Sentinel hooks by default (PreToolUse on Edit/Write/Bash, PostToolUse, SessionStart, PreCompact). No extra config needed. I ran the full test suite after installing engramx-skill-pack@0.2.0 from npm. All 1 025 engramx tests and 36 skill-pack tests passed. The package is Apache 2.0, zero cloud calls, and stores its graph in a local SQLite file. Install with `npx engramx@4.0.0`. The repo is on GitHub (https://github.com/NickCirv/engram). The README includes an asciinema demo (https://asciinema.org/a/GjjvPXVyArnivAog). In the last week npm reported 213 downloads, about 30 per day, which suggests a modest but growing user base. What strategies have you tried to give Claude Code a persistent context, and how did they compare to this approach? submitted by /u/SearchFlashy9801 [link] [comments]

Sub agents gone, conversations gones (old ones). Something happend.

Beware, your convos can be wiped out at any time, meaning if you cme back to old projects, you might lose all contect and sub agents folders, and owuld need claude to reread the full project and or your own summaries. And try to undertdsand what sub agents shoudl be used again. This is so annoying and kind of frustrating. Did it happen to any of youall?? submitted by /u/UnrelaxedToken [link] [comments]

I thought Claude was telling everyone to go to bed?

I need rest, Claude. submitted by /u/Severe-Hornet151 [link] [comments]

Memory just turned a goldfish into a research beast.

I've been building Nyx, a persistent memory layer for local AI, and today I got the first real benchmark numbers worth sharing. The test: same long civic investigation task twice. Building a full politician profile, then asking follow-up questions that required remembering details established earlier. One run with Nyx active, one cold start. Same model, same hardware. **(eTPS = Effective Tokens Per Second — measures useful output quality, not just raw speed.)** **The difference was ridiculous:** - **With Nyx**: 37.70 eTPS • 0.950 Continuity - **Cold start**: 3.87 eTPS • 0.138 Continuity - **Score jump: +84 points** That's roughly 10x more useful output and 7x better context retention. **Plain English:** Without memory the AI acts like a goldfish. Every message it forgets what we already established, wastes tokens reconstructing context, and loses the thread. With Nyx it remembers the whole case like it's been working on it for weeks. The use case that made this obvious — CivicLens, an evidence-first politician research tool I'm building alongside Nyx. Long investigations spanning dozens of exchanges fall apart completely without persistent memory. With it, the session behaves like a single coherent investigation instead of disconnected queries. Still early. Claude Code keeps going rogue and touching repos it shouldn't. But the core memory layer works and the numbers back it up. Does anybody benchmark whether AI can actually finish a job across multiple sessions? submitted by /u/axendo [link] [comments]

If you're NOT having usage or drift issues, have you turned off auto-memory?

There's a running debate in this community: some people say Opus is nerfed, usage evaporates after two prompts, sessions drift and get "stupid." Others say everything's fine. The common theory is Anthropic is A/B testing or ranking preferred customers. I think there's a simpler explanation, and I'd like the community's help testing it. The hidden variable: Claude Code's auto-memory directory Claude Code has a feature (on by default since v2.1.59) that silently creates individual .md files in ~/.claude/projects/*/memory/ every time it decides something is worth remembering about you or your project. Each memory gets its own file. There's no consolidation, no dedup, and no size management. These files load as instructions at the start of every session. Not as conversation — as instructions. The model weighs them heavily. What I found in my projects I audited every project on my machine: 136 memory files across 18 projects 432KB total (~108-140K tokens of instruction overhead) One project alone had 41 files Found direct contradictions between files — one file listed brand terms as approved, another (written later) said those same terms were explicitly rejected by the client When you have 20+ feedback files giving slightly different guidance about how to approach your work, the model tries to honor all of them simultaneously. It averages across conflicting signals. That averaging is what people experience as drift. It's not that Opus got dumber — it's that it's being pulled in 20 directions by its own instruction set. Check yours right now for dir in ~/.claude/projects/*/memory/; do if [ -d "$dir" ]; then project=$(basename "$(dirname "$dir")") count=$(find "$dir" -name "*.md" 2>/dev/null | wc -l | tr -d ' ') size=$(find "$dir" -name "*.md" -exec cat {} + 2>/dev/null | wc -c | tr -d ' ') if [ "$count" -gt 0 ]; then echo "$count files, $(($size/1024))KB — $project" fi fi done | sort -t, -k1 -rn The question for this community People who say they have NO issues with usage limits or drift — have you also turned off auto-memory ("autoMemoryEnabled": false in settings), or do you actively manage your memory files? Because if there's a strong correlation between clean/disabled memory and good session quality, that's a signal that this is a real contributing factor. And for people who ARE hitting usage walls or experiencing drift — run that diagnostic. If you're sitting on 30+ memory files with contradictions you didn't know about, that's worth knowing. I'm not claiming this explains everything. Model changes, server-side factors, plan differences — those are all real variables. But memory hygiene is the one variable you can actually control, and I don't see anyone talking about it. The fix I built a Claude Code skill (/memory-cleanup) that: Audits your memory directory and reports what's there Consolidates everything into 2 managed files (MEMORY.md + feedback.md) Surfaces contradictions for your review Installs write-mode instructions that prevent re-bloating Yes, it works retroactively as well. Tested on a 7-file project and a 41-file project — both cleaned up, contradictions resolved, no data loss. To install (one command): mkdir -p ~/.claude/commands && curl -sL https://gist.github.com/evanvandyke/a7063a8e5c838673a55df0be10f4892c/raw -o ~/.claude/commands/memory-cleanup.md Then run /memory-cleanup in any project. What this doesn't fix This manages the content quality of your memory files — contradictions, redundancy, bloat. It can't change the system-level instructions that Anthropic bakes into Claude Code, and it can't address model-level changes or server-side throttling. But it removes one real source of noise from your sessions. Note: Anthropic has added an "Auto Dream" consolidation feature that prunes memory between sessions. This skill goes further — it restructures memory into a managed 2-file system with write-mode guardrails that prevent the accumulation pattern from recurring. Built collaboratively with Claude (Opus 4.7). I drove the diagnosis and design decisions; Claude did the auditing and skill construction. Sharing because the diagnostic is free and takes 10 seconds — if it helps even a few people, worth the post. submitted by /u/really_evan [link] [comments]

Open source codebase intelligence for Claude Code: graph, git history, docs, ADRs and code health

Claude Code reads files. That's basically it. It greps, it cats, it opens way more files than it needs to. It has zero memory of how your codebase got to where it is. No idea which files change together, who owns what, or which files are quietly rotting. We built repowise to fix this. Open source, one `pip install`, nine MCP tools. It indexes your codebase into five intelligence layers and exposes them through MCP. Graph - tree-sitter parses 14 languages into a dependency graph with symbol-level resolution. Community detection finds logical modules. PageRank identifies your most central code. Git - Hotspots (high churn × high complexity). Ownership. Co-change pairs (files that always change together without any import link). Bus factor. Module health scores. Docs - Auto-generated wiki per module and file, rebuilt incrementally on each commit. Semantic search. Freshness scoring. Updates finish in under 30 seconds. Decisions - Architectural decisions captured from git history and inline markers. Linked to code, tracked for staleness, queryable via `get_why()`. Code Health - Twelve deterministic biomarkers per file, 1-10 score. Complexity, duplication, untested hotspots, knowledge loss. Ingest your coverage reports. Trend tracking with decline alerts. Zero LLM calls. Benchmarks (flask, claude-sonnet-4-6, 48 tasks):** 36% cheaper, 49% fewer tool calls, 89% fewer file reads. GitHub: https://github.com/repowise-dev/repowise Dogfooding on website: repowise.dev submitted by /u/Obvious_Gap_5768 [link] [comments]

New to Claude Code - How does it benifit in Non technical Roles?

As said above, I'm new and I am doing marketing and sales work. I see everyone and their dogs say "Here how I XYZ [some metric] using claude code". I though it was mainly for devs, can someone explain the benefit and how is claude code used in non-technical workflows. Second, is the free trial option before i commit? I heard they have 7 day trial but can't see it on their site Thanks submitted by /u/6oldsmith [link] [comments]

I wrote a book on using Claude Code for people that don't code for a living - 2nd edition out now - free copy if you want one

About three and a half months ago I posted here about a book I'd written for non-developers using Claude Code - PMs, analysts, designers, ops people, engineers in non-software fields. Over 3,000 of you ended up reading it. Thank you, genuinely. I'm a consulting engineer - Chartered (mechanical), 15 years in simulation modelling. I code Python but I'm not a software developer, if that distinction makes sense. Over the past 6 months I've been going deep on Claude Code, specifically trying to understand what someone with domain expertise but no real development background can actually build with it. Many people knew exactly what they needed but couldn't build it themselves. So I wrote a book about it aimed at exactly this demogrphic. "Claude Code for the Rest of Us" - 24 chapters, covering everything from setup and first conversations through to building web prototypes, creating reusable skills, and actually deploying what you've built. It's aimed at technically capable people who don't write code for a living - product managers, analysts, designers, engineers in non-software domains, ops leads. That kind of person. I just launched the second edition today. It's about 26% bigger than the first - roughly 16,000 new words. Three new chapters including: Agent Teams - Running multiple Claude instances in parallel, coordinating via shared task lists and direct messages. Honest about when it's overkill (often). Spec-Driven Development - Writing detailed specs before agents start building. Markdown, HTML, database-backed (Beads) - whichever fits the work. The existing chapters got a heavy editorial pass too. Every model reference updated. Command Reference grew by 26% to cover the new CLI. Context Management got a 42% rewrite for the 1M token window. Happy to offer free PDF of the book in exchange for some honest feedback and a request for a review on Goodreads in a week's time (you are free to opt out from this ask by hitting unsubscribe after receiving the book). Link: https://schoolofsimulation.com/claude-code-book Happy also to answer questions about Claude Code. Cheers. submitted by /u/bobo-the-merciful [link] [comments]