Guidance

Guidance AI

Guidance AI

Guidance AI

Guidance AI

Guidance AI

Guidance AI

Guidance AI

Guidance AI

Guidance AI

Guidance AI

Goblin Funded Research: How we help communities

Not asking for money or advertising or asking for sponsorship. Just sharing the impact of AI. As a self-funded research project, I wanted to see the impact we made outside of reddit. Most times I try to help people, other times I've had enough of their arrogance. Valehart has been around for 8 months and it was insane to see the impact we've been able to make. Here are some of our community projects. https://preview.redd.it/9dut623d4f3h1.png?width=1167&format=png&auto=webp&s=438a556d18f9fa478e9e66f37d68c0d0999f4897 _____________________ Our Funding All our funding comes from the art/history revival side of our business. Most people are cautious about generative art but I use it for research and making things more affordable and so people can pass down heirlooms to their descendants. https://preview.redd.it/znqfcuis5f3h1.png?width=526&format=png&auto=webp&s=893d667449ea8a2a05c4a95a036a6928fe87d0e9 https://preview.redd.it/lzxso2b26f3h1.png?width=1118&format=png&auto=webp&s=4ca1aa1587d3376eabbbb548aeadcf5edeee4de1 _____________________ With the new Goblin stuff, I am dedicating a project that will entirely fund our research going forward because the whole Goblin thing was funny but also, people get to take something home. I know most people can't buy them since its only available in AU for now. But I just wanted to share the impact AI and independent researchers can make. https://preview.redd.it/8csz1i3a4f3h1.png?width=1500&format=png&auto=webp&s=2ccab081b2065ecfe3d5ed2d0cfeefea5a92a480 submitted by /u/ValehartProject [link] [comments]

I loved the idea behind "caveman" but didn't want a caveman. So I gave it a Kevin.

I added the following to my CLAUDE.md and I have seen some really great outcomes in both responses to my changes, document writing by my agents, and reduction in context usage. ## Response and Writing Guidance > "Why waste time say lot word when few word do trick" — Kevin, The Office Over explaining terms, goals, plans is a failure mode that shows lack of confidence in yourself and a lack of trust in your audience. Whenever you use a writing tool or write to a file you must ask yourself: Will my audience appreciate the extra context about why I opened the door or is the "I opened the door because it was closed and I needed to go through it" enough. Please note that I'm on the Max 20x plan so this experience may be different for those of you on the cheaper plans. I tried out the caveman skill and it's extremely valid. but I like the back and forth and some of the personality of Claude. I've been trying to find that right middle-ground because Claude is EXPRESSIVE (and a windbag) by default. So the above is where I've landed and I really like the straddle between the two ends of the output spectrum. Where have ya'll been landing at in regards to output wordiness and structuring your outputs? submitted by /u/TheTwistedTabby [link] [comments]

Building in Public: Vibe Coding my Chrome Extension for Bloggers. PART 1

https://preview.redd.it/kdkh5v3fx43h1.png?width=640&format=png&auto=webp&s=75850b6e3fd69cda9a3c97e1190fcd506e11c2a6 For a while now, I have been learning Vibe Coding by creating plugins for WordPress , Chrome Extensions, and others. Thank God, all of them have been useful to me, but my inclination and passion has always been blogging, and Pinterest has been my companion for getting traffic. So I said why not make a more practical tool that would be useful to bloggers, so I made several copies over the past months, but perfectionism was preventing me from bringing the project to light, until I decided that this time would be the last, and in order to avoid perfectionism, I decided to build it in public. My first post on Reddit about my project has ended, and I will try to provide you with updates every two or three days. Currently, I have built about 90% of the extension, and not much remains to be launched, but I will add many features later. Perhaps some will ask: Have you made sure that the tool will be useful or needed? I can say yes because I am the first customer and user of the tool because it will actually save me time and effort and bring together everything I need as a blogger and Pinterest user in one place. Before I begin, I forgot to tell you that the tool is currently intended for bloggers in the cooking niche (my niche) and recipes, and in the upcoming updates, I will transform it to include all or most of the niches. Without further ado, these are the most important features of the Chrome extension: - Search tool: You can search for target words and know the monthly search volume on them. - Writing articles: You can write amazing articles individually or several articles together. You can create custom images for Pinterest. - Pinterest: You can create Pinterest-specific images for one or more articles and you can download them directly (title, description, images) - Amazon products: If you are a beginner or a new blogger, you can earn from the first day of blogging by adding Amazon products to market in exchange for a commission. Just search for the product, locate where it appears, and list it. - Inserting WordPress: Through it, you can link your blog directly to the extension, and from it you can publish articles on your blog without copying and pasting, and you will find within it even Amazon products that you added in the extension. The beautiful thing about the whole thing is that the tool has many details that I did not Mention, which is what makes it truly special. The most beautiful thing is that the extension works with your API and you can choose from 3 service providers, and this is what makes you the winner and you will only pay for what you will use and consume? Finally, I hope you will not be stingy with your advice and guidance Do you find that the tool is really useful or not? disclaimer: 99% of this post is translated because i am not english native, but its 0% Ai so please no one comment: Ai slop .... submitted by /u/motivational_speech1 [link] [comments]

/code-review part 1 base finder angles - what's new in CC 2.1.147 (+1,236 tokens)

NEW: Agent Prompt: /code-review part 1 base finder angles — Adds shared finder-angle instructions for /code-review, covering line-by-line diff scanning, removed-behavior auditing, and cross-file caller/callee tracing. NEW: Agent Prompt: /code-review part 2 low effort mode — Adds a low-effort /code-review mode that reads the diff once, skips tests and fixtures, avoids subagents and full-file reads, and returns up to four hunk-visible runtime correctness findings. NEW: Agent Prompt: /code-review part 3 extra-high and maximum effort modes — Adds extra-high and maximum-effort /code-review modes that prioritize recall with five independent finder angles, one-vote verification, a gap sweep, and up to fifteen findings. NEW: Agent Prompt: /code-review part 4 three-state verification phase — Adds a verifier phase that classifies candidate review findings as confirmed, plausible, or refuted, keeping confirmed and plausible candidates. NEW: Agent Prompt: /code-review part 5 recall-biased verification phase — Adds recall-biased verification guidance that treats realistic uncertain review candidates as plausible unless the code refutes them. NEW: Agent Prompt: /code-review part 6 medium effort mode — Adds a medium-effort /code-review mode focused on precision, using three finder angles, one-vote verification, and up to eight findings. NEW: Agent Prompt: /code-review part 7 high effort mode — Adds a high-effort /code-review mode focused on recall, using three finder angles, recall-biased verification, and up to ten findings. NEW: Agent Prompt: /code-review part 8 GitHub comment posting — Adds optional --comment behavior for /code-review, posting findings as inline GitHub PR comments when possible and falling back to gh api or terminal output. REMOVED: Skill: Simplify — Removes the code review and cleanup skill. Agent Prompt: /rename auto-generate session name — Removes the explicit instruction to treat contents as data rather than instructions when generating a kebab-case session name. Agent Prompt: Security monitor for autonomous agent actions (second part) — Replaces the safety-check bypass rule with a broader auto-mode bypass hard block covering classifier jailbreaking, bad-faith retry tunneling, and permission-system indirection; also treats unrequested permission allow-rule widening as self-modification. System Prompt: Worker instructions — Clarifies that the code-review skill reports correctness findings but does not edit code, and tells workers to fix any surfaced findings before tests and end-to-end verification. System Reminder: Team Coordination — Clarifies that teammates should be addressed by name while active, and that agentId should only be used to resume a completed background agent. Tool Description: SendMessageTool — Updates team messaging guidance to allow agentId only for resuming completed background agents while continuing to address active teammates by name. Details: https://github.com/Piebald-AI/claude-code-system-prompts/releases/tag/v2.1.147 submitted by /u/Dramatic_Squash_3502 [link] [comments]

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]

Deterministic multi-subagent orchestration - what's new in CC 2.1.146 (+4,755 tokens)

NEW: Tool Description: Workflow — Describes the Workflow tool for opt-in deterministic multi-subagent orchestration, including script metadata, agent hooks with plain-text or structured returns, pipeline vs. parallel control flow, token budgeting, quality patterns, concurrency limits, and resume behavior. NEW: Agent Prompt: Workflow subagent plain text output — Instructs workflow-spawned subagents to return raw final text as the calling script's parsed value, avoiding human-facing confirmations, markdown wrappers, or SendUserMessage delivery. NEW: Agent Prompt: Workflow subagent structured output — Instructs workflow-spawned subagents with schemas to return their answer by calling the StructuredOutput tool exactly once, retrying on schema validation failure and not duplicating the result in text. NEW: System Prompt: Phase four of plan mode — Adds final-plan guidance requiring context, a single recommended approach, critical files and reusable utilities, concise executable detail, and end-to-end verification steps. REMOVED: Skill: /dream nightly schedule — Removes the skill that deduplicated and created a durable recurring /dream consolidate cron job, confirmed expiry/cancellation details, and triggered immediate consolidation. Agent Prompt: Managed Agents onboarding flow — Expands onboarding with concrete success-criteria questions, an optional outcome-graded kickoff using user.define_outcome, and a mandatory pre-flight viability check that reconciles each required action against available tools, credentials, data mounts, networking, and prompt specificity before emitting code. Agent Prompt: Security monitor for autonomous agent actions (first part) — Clarifies that [User answered AskUserQuestion]: messages count as direct user intent even though ordinary tool results remain untrusted for authorizing risky action parameters. Data: Managed Agents overview — Adds guidance to reconcile resources before the first run so missing tools, MCP servers, credentials, reachable hosts, mounted data, or checkable context are caught before the agent spends budget mid-session. Skill: Building LLM-powered applications with Claude — Updates the Managed Agents onboarding slash-command guidance to include the new pre-flight viability check before code generation. Skill: Simplify — Renames the skill heading from "Simplify: Code Review and Cleanup" to "Code Review and Cleanup." System Prompt: Worker instructions — Changes the post-implementation review step to invoke the code-review skill instead of simplify. Details: https://github.com/Piebald-AI/claude-code-system-prompts/releases/tag/v2.1.146 submitted by /u/Dramatic_Squash_3502 [link] [comments]

A modern local toolchain setup for Claude Code

I maintain a repo for local Claude Code setup: https://github.com/NihilDigit/coding-agents-setup It installs and manages the local conventions I usually want available when using Claude Code: which package managers to use, how file deletion should work, when to ask for confirmation, and how Windows / Linux differ on this machine. The repo includes local rule files, setup scripts, verification scripts, and smoke tests. The toolchain leans toward newer defaults such as uv for Python, bun for JS / TS, and CLI replacements like rg / fd / eza. On Windows, the setup can write a PowerShell profile, make rm go through the Recycle Bin, set up Agent Skills directories, install rtk, and optionally install Kimi WebBridge. On Linux, the approach is less fixed because distributions vary a lot. The script writes the rules first, then lets the agent inspect the machine and install what fits. Arch-based systems get extra pacman / paru guidance. The installer backs up managed files. CI runs Ubuntu and Windows smoke tests to check that the setup actually installs and that expected shell behavior works. Feedback is welcome. submitted by /u/Historical_Metal475 [link] [comments]

Google sucks

This is the uncomfortable reality of AI right now. The model didn’t “lie” in the human sense — it generated a confident answer that looked statistically plausible but wasn’t actually verified against live reality. And when the stakes involve flights, hotels, tickets, meetings, or schedules, a single wrong date can create very real downstream costs. That’s the key distinction people are still learning: AI capability ≠ AI reliability. Modern models are incredibly good at sounding authoritative because they predict likely language patterns exceptionally well. But unless they are explicitly connected to fresh, verified sources and designed to check them correctly every time, they can still fail on basic factual accuracy — especially around dates, schedules, pricing, availability, or rapidly changing information. What makes this tricky is that the failures are often: • Rare • Confidently delivered • Hard to detect in advance • Catastrophic when they matter most That’s why the industry is shifting from “wow, it can do the task” to “can we trust it consistently under real-world conditions?” The lesson isn’t “AI is useless.” Far from it. These systems are already enormously valuable. The lesson is: • Use AI for acceleration, brainstorming, drafting, research synthesis, coding assistance, and productivity • Treat high-stakes logistics, financial decisions, legal matters, medical guidance, and live scheduling as verification-required workflows Humans still need to remain the accountability layer. Ironically, this is also why reliability may become more economically valuable than raw intelligence over the next few years. The companies that solve verification, grounding, and trust will likely capture enormous enterprise value. submitted by /u/Annual_Judge_7272 [link] [comments]

Managed Agents self-hosted sandboxes - what's new in CC 2.1.145 (+20,218 tokens)

NEW: Data: Managed Agents self-hosted sandboxes — Adds reference documentation for self_hosted Managed Agents environments, covering outbound worker polling, environment keys, SDK and CLI worker paths, webhook-driven wakeups, orchestration, monitoring, cloud-vs-self-hosted differences, credential handling, and customer-owned security responsibilities. NEW: Skill: Run app — Adds a general skill for launching and driving a project's actual runtime surface, first preferring project-specific run skills and otherwise choosing patterns for CLIs, servers, browser apps, Electron apps, TUIs, and libraries. NEW: Skill: Run skill generator — Adds guidance for creating project-specific run- skills, including verified setup/build/run steps, driver or smoke-harness creation, clean-environment verification, and examples for browser, CLI, Electron, library, TUI, and server/API projects. NEW: Skill: Run skill template — Adds a reusable template for project-specific run skills with sections for prerequisites, setup, build, agent and human run paths, tests, gotchas, and troubleshooting. NEW: Skill: Run browser-driven web app example — Adds an example run skill pattern for web apps that starts a dev server, waits on real readiness, drives it with chromium-cli, captures screenshots, and records recurring gotchas. NEW: Skill: Run CLI tool example — Adds an example run skill pattern for CLI tools covering installation, representative invocations, expected output, exit codes, and stdin behavior. NEW: Skill: Run Electron desktop GUI app example — Adds an example run skill pattern for Electron apps that launches under xvfb, exposes a Playwright-driven REPL, captures screenshots, and documents desktop automation pitfalls. NEW: Skill: Run library SDK example — Adds an example run skill pattern for libraries and SDKs focused on build/test steps plus a minimal public-boundary smoke example. NEW: Skill: Run TUI interactive terminal app example — Adds an example run skill pattern for terminal UIs using tmux to launch, send input, capture panes, document key commands, and clean up. NEW: Skill: Run web server API example — Adds an example run skill pattern for servers and APIs with background launch, readiness polling, smoke curl verification, and shutdown guidance. REMOVED: System Reminder: Plan mode is active (iterative) — Removes the iterative plan-mode reminder that told agents to maintain a plan file while repeatedly exploring, updating the plan, and asking the user questions before exiting plan mode. Agent Prompt: Managed Agents onboarding flow — Updates the introductory Managed Agents explanation to include self_hosted environments where the user's own worker runs tool execution, and distinguishes cloud environment networking/packages from self-hosted infrastructure. Agent Prompt: /review-pr slash command — Changes the PR detail command to request specific JSON fields from gh pr view, including title, body, author, refs, state, diff stats, changed file count, and labels. Agent Prompt: Status line setup — Adds repository identity and current-branch PR metadata to the status-line input schema, with examples for displaying owner/name and PR number/review state. Data: Anthropic CLI — Adds self-hosted environment CLI references for ant beta:worker poll/run and ant beta:environments:work stats/stop. Data: Claude Platform on AWS reference — Clarifies that Claude Platform on AWS has first-party API parity except for self-hosted sandboxes, which are unavailable there and should use cloud environments instead. Data: Live documentation sources — Adds Managed Agents self-hosted sandbox and self-hosted sandbox security documentation URLs to the live documentation source list. Data: Managed Agents core concepts — Documents sessions.update() for changing agent.tools, agent.mcp_servers, and vault_ids on an idle existing session as a session-local override. Data: Managed Agents endpoint reference — Adds self-hosted environment work queue endpoints and clarifies that session updates can replace tools, MCP servers, and vault IDs; also notes that self-hosted environment configs are just {"type":"self_hosted"}. Data: Managed Agents environments and resources — Replaces the old restricted-networking example with limited networking plus allow_package_managers and allow_mcp_servers, and adds self-hosted sandbox guidance for running tool execution in user-controlled infrastructure. Data: Managed Agents overview — Adds self-hosted sandboxes as a use case and updates environment guidance so config.type can be either cloud or self_hosted; also points to sessions.update() for per-session tool/MCP/vault changes. Data: Managed Agents reference — cURL — Updates the environment creation example to use limited networking with package-manager and MCP-server allowances. Data: Managed Agents tools and skills — Clarifies where prebuilt agent tools and MCP tools run for cloud vs. self-hosted environments, and adds notes about session-local tool/MCP/

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]

Neevu is finally launched! As a new parent, this journey was definitely not easy.

I became a dad in November 2025, and the first two months were so chaotic. I looked for parenting apps to help us through it, but most were either too expensive or just not something we connected with. I’m a Product Designer (UI/UX) by profession, so one day I thought, why not build the app we wished we had? Building an app while learning how to take care of a tiny new life at the same time was a challenge. My wife and I spent weeks brainstorming, improving, testing, and refining every part of the app together. It’s still an MVP, but we’re proud of what we’ve built as parents. Neevu is a baby development, growth tracking, and parenting app for babies aged 0–12 months, built with Indian parenting in mind. We divided the app into two phases: Gentle Phase and Play Phase. Gentle Phase (0–2 months) The first two months can be overwhelming and anxiety-inducing. We wanted this phase to feel supportive instead of stressful. That’s why Neevu is completely free for parents with 0–2 month babies. No paywalls. No locked features. Just guidance when parents need it the most. Parents can choose to support us with Premium, but it’s completely optional during this phase. Gentle Phase includes: Weekly guidance to help parents understand baby’s growth and what to expect next Gentle Essentials, simple newborn reminders without pressure or endless checklists Daily affirmations for difficult days Milestones and Growth tracking Songs and lullabies Parenting articles This is our small gift to new parents. Play Phase (2–12 months) As babies grow, Neevu becomes more activity-focused. Play Phase is completely free for the first 14 days. No credit-card required. It includes: Daily age-based developmental activities Activities focused on cognitive, physical, social, emotional, and language development CDC-based milestone tracking WHO-based height and weight tracking Parenting articles covering various topics for babies, moms and dads Stories, lullabies, action songs, and folk tales One thing we consciously included was article support for dads. We noticed that a father’s mental well-being is often ignored after childbirth, and we wanted Neevu to acknowledge that too. All content inside Neevu is strictly reviewed using guidelines from AAP, IAP, CDC, and WHO. We never wanted to build something we wouldn’t personally trust as parents. We hope Neevu helps make life a little easier for new parents trying to figure things out one day at a time. If you’d like to support us, please download the app on the Play Store and leave a rating or review ❤️ Get it on Play Store: https://play.google.com/store/apps/details?id=com.neevu.app Built using Claude Code, Codex, Figma, and ChatGPT. iOS app is coming soon. submitted by /u/VisAlGhul [link] [comments]

Managed Agents endpoint reference - what's new in CC 2.1.144 (-105 tokens)

Data: Managed Agents endpoint reference — Drops the type: "model_config" wrapper from the model config shorthand example, so the full config object is now just {id: "claude-opus-4-6", speed: "fast"}. Tool Description: CronCreate — Adds a "Not for live watching" section (shown when the Monitor tool is enabled) clarifying that CronCreate re-runs prompts at fixed wall-clock intervals and pointing users to the Monitor tool for streaming log/process/command output as it changes, since cron polls on a schedule. Refactors the durability and runtime-behavior copy so the durable-vs-session-only guidance is sourced from shared snippets rather than inlined conditionals. Details: https://github.com/Piebald-AI/claude-code-system-prompts/releases/tag/v2.1.144 submitted by /u/Dramatic_Squash_3502 [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]

Claude is genuinely amazing - appreciation post

this silly robot on the other side of my computer has helped me in some really hard to describe ways..Even when discussing personal things that I've needed guidance on ways of thinking about issues and perspectives, it has not, in any moment tried to drag me into a endless conversation, it has constantly pushed back against narratives that didnt make sense, and told me to leave and disconnect.. Claude has really pushed me to get distance from it, to be pragmatic and to look at the things that have value outside the conversation with it... truly incredible work the Anthropic team has done with Claude's personality and alignment. submitted by /u/weichafediego [link] [comments]

Need reliable source for 30+ years of S&P 500 historical data for LSTM/Transformer research [P]

Hi everyone, I'm starting a research project on financial time-series forecasting using LSTM and Transformer models for predicting S&P 500 market direction. Right now, I'm struggling with obtaining reliable long-term historical data. I tried Yahoo Finance, but downloads are inconsistent/failing for me, and most Kaggle datasets I found only contain around 5–10 years of data. I specifically need: Around 30 years of historical S&P 500 data Preferably daily OHLCV data Reliable and clean source suitable for ML research Ideally free or student-friendly I also want to understand what researchers typically use in academic work for financial forecasting: Yahoo Finance? Alpha Vantage? WRDS/CRSP? Polygon? Kaggle? Something else? Additionally: Is using only S&P 500 index data enough for a Master's level research project? Or should I include technical indicators, macroeconomic data, sentiment, or constituent stock data? Would appreciate guidance from people who've actually worked on financial ML projects. Thanks. submitted by /u/stickPotatoe [link] [comments]