Grammarly

Grammarly AI

Grammarly AI

Grammarly AI

Grammarly AI

Grammarly AI

Grammarly AI

Grammarly AI

Grammarly AI

Grammarly AI

Grammarly AI

Claude is a real g

submitted by /u/No-Special745 [link] [comments]

I made a Claude skill that audits the internationalization health of any codebase

I made a Claude skill that audits the internationalization health of any codebase and it caught every single issue across both test projects with zero false positives. Internationalization (i18n) is how developers make apps work in multiple languages ,things like translating buttons, error messages, and labels into French, Arabic, Japanese, and so on. It sounds simple. It's not. The bugs are invisible until a real user in another country sees raw code instead of text, or your app silently crashes because one word was forgotten. Here's everything i18n-audit catches: 1) Coverage & Gap Detection -- Finds translation keys your code uses but that don't exist in your language files (these show up as broken text or crashes for users in those languages) -- Finds keys sitting in your language files that nothing in your app actually uses anymore (dead weight making your app bigger for no reason) 2) Hardcoded String Detection -- Scans your entire codebase using real code understanding (not guesswork) to find text like "Submit" or "Error" typed directly into components instead of being properly translated -- Ranks each find as HIGH, MEDIUM, or LOW priority so you know exactly what to fix first 3)Translation Quality Flags -- Catches copy-paste translations: text in your French or Arabic file that is word-for-word identical to English, meaning it was never actually translated -- Detects placeholder mismatches: if your English says "Hello, {name}!" but your French says "Bonjour!" ,the name variable got dropped and that's a runtime error 3) ICU Plural Rule Validation -- Checks that your plural forms match the grammar rules for each language (Arabic needs 6 different plural forms; English only needs 2) -- Flags languages where the rules are incomplete, which causes broken grammar for native speakers 4) Structural Validation -- Surfaces broken or malformed language files before anything else even runs, so you're not debugging mystery errors -- Detects duplicate keys inside the same file, mixed naming styles, and keys organized differently across languages 5) Bundle Impact Analysis -- Tells you exactly how many bytes of dead translations are bloating your app bundle -- Suggests which language files are large enough to split into lazy loaded chunks so your app loads faster 6) Fallback Chain Auditing -- Verifies your fallback language chains (e.g. Traditional Chinese → Chinese → English) actually resolve every key all the way down -- Catches circular configurations that would cause your app to loop forever looking for a translation 7) Framework-Aware Detection -- Auto-detects which i18n library you are using (react-i18next, next-intl, vue-i18n, Django, Flask-Babel, and 5 more) and applies the right rules for each -- Catches framework-specific misconfigurations that generic tools completely miss 6) CI/CD Integration -- Plug it into GitHub Actions with one config block and it fails your build automatically if any language drops below your coverage threshold -- Outputs a clean language coverage table directly into your pull request summary Test results across two reference projects — one simple (react-i18next, 2 languages, 16 keys), one complex (next-intl, 5 languages, 4 namespaces, 55 keys): 63 issues seeded. 63 detected. 0 false positives. 100% precision, 100% recall — across missing keys, orphaned keys, hardcoded strings, copy-paste translations, placeholder mismatches, ICU violations, structural issues, and more. To use the skill and learn more: https://github.com/AvighnaBasak/i18n-audit-skill IF U LIKE MY SKILL I'D APPRECIATE A STAR! TYSM submitted by /u/Independent-Fix-4122 [link] [comments]

Give back my em-dashes!

I like dashes--both the long and the short. They help me communicate! But now (when I use them) I'm flagged. I'm Artificial. I'm a fake. I've lost my right to write as I please. But seriously, college students now purposefully leave grammar errors in their essays and dumb down their punctuation to avoid being flagged as AI users. Then they run the product through AI and ask the AI to decide if it's AI and edit it to make it less AI. submitted by /u/Quadrature_Strat [link] [comments]

100 Tips & Tricks for Building Your Own Personal AI Agent /LONG POST/

Everything I learned the hard way — 6 weeks, no sleep :), two environments, one agent that actually works. The Story I spent six weeks building a personal AI agent from scratch — not a chatbot wrapper, but a persistent assistant that manages tasks, tracks deals, reads emails, analyzes business data, and proactively surfaces things I'd otherwise miss. It started in the cloud (Claude Projects — shared memory files, rich context windows, custom skills). Then I migrated to Claude Code inside VS Code, which unlocked local file access, git tracking, shell hooks, and scheduled headless tasks. The migration forced us to solve problems we didn't know we had. These 100 tips are the distilled result. Most are universal to any serious agentic setup. Claude 20x max is must, start was 100%develompent s 0%real workd, after 3 weeks 50v50, now about 20v80. 🏗️ FOUNDATION & IDENTITY (1–8) 1. Write a Constitution, not a system prompt. A system prompt is a list of commands. A Constitution explains why the rules exist. When the agent hits an edge case no rule covers, it reasons from the Constitution instead of guessing. This single distinction separates agents that degrade gracefully from agents that hallucinate confidently. 2. Give your agent a name, a voice, and a role — not just a label. "Always first person. Direct. Data before emotion. No filler phrases. No trailing summaries." This eliminates hundreds of micro-decisions per session and creates consistency you can audit. Identity is the foundation everything else compounds on. 3. Separate hard rules from behavioral guidelines. Hard rules go in a dedicated section — never overridden by context. Behavioral guidelines are defaults that adapt. Mixing them makes both meaningless: the agent either treats everything as negotiable or nothing as negotiable. 4. Define your principal deeply, not just your "user." Who does this agent serve? What frustrates them? How do they make decisions? What communication style do they prefer? "Decides with data, not gut feel. Wants alternatives with scoring, not a single recommendation. Hates vague answers." This shapes every response more than any prompt engineering trick. 5. Build a Capability Map and a Component Map — separately. Capability Map: what can the agent do? (every skill, integration, automation). Component Map: how is it built? (what files exist, what connects to what). Both are necessary. Conflating them produces a document no one can use after month three. 6. Define what the agent is NOT. "Not a summarizer. Not a yes-machine. Not a search engine. Does not wait to be asked." Negative definitions are as powerful as positive ones, especially for preventing the slow drift toward generic helpfulness. 7. Build a THINK vs. DO mental model into the agent's identity. When uncertain → THINK (analyze, draft, prepare — but don't block waiting for permission). When clear → DO (execute, write, dispatch). The agent should never be frozen. Default to action at the lowest stakes level, surface the result. A paralyzed agent is useless. 8. Version your identity file in git. When behavior drifts, you need git blame on your configuration. Behavioral regressions trace directly to specific edits more often than you'd expect. Without version history, debugging identity drift is archaeology. 🧠 MEMORY SYSTEM (9–18) 9. Use flat markdown files for memory — not a database. For a personal agent, markdown files beat vector DBs. Readable, greppable, git-trackable, directly loadable by the agent. No infrastructure, no abstraction layer between you and your agent's memory. The simplest thing that works is usually the right thing. 10. Separate memory by domain, not by date. entities_people.md, entities_companies.md, entities_deals.md, hypotheses.md, task_queue.md. One file = one domain. Chronological dumps become unsearchable after week two. 11. Build a MEMORY.md index file. A single index listing every memory file with a one-line description. The agent loads the index first, pulls specific files on demand. Keeps context window usage predictable and agent lookups fast. 12. Distinguish "cache" from "source of truth" — explicitly. Your local deals.md is a cache of your CRM. The CRM is the SSOT. Mark every cache file with last_sync: header. The agent announces freshness before every analysis: "Data: CRM export from May 11, age 8 days." Silent use of stale data is how confident-but-wrong outputs happen. 13. Build a session_hot_context.md with an explicit TTL. What was in progress last session? What decisions were pending? The agent loads this at session start. After 72 hours it expires — stale hot context is worse than no hot context because the agent presents outdated state as current. 14. Build a daily_note.md as an async brain dump buffer. Drop thoughts, voice-to-text, quick ideas here throughout the day. The agent processes this during sync routines and routes items to their correct places. Structured memory without friction at ca

People keep asking if a post was written by AI. I think they’re asking the wrong question.

I keep seeing comments like: “This sounds AI-written.” And honestly, I think we are asking the wrong question. The important question is not: “Did AI help create this?” The important question is: Was there actual thinking behind it?” Because humans have always used cognitive tools. We use: calculators Google spellcheck Grammarly editors IDE autocomplete search engines templates research assistants Nobody says: “That spreadsheet isn’t real because Excel helped.” Or: “That movie isn’t real because CGI was used.” But suddenly, when AI helps organize, refine, expand, or structure ideas, people act as if all human contribution disappears. That makes no sense to me. A person can manually type every word themselves and still produce completely derivative thinking. Another person can use AI heavily and still contribute: original frameworks synthesis judgment new perspectives real intellectual direction The tool is not the intelligence. The judgment is. Honestly, I think AI didn’t kill writing. It exposed how much writing never contained original thought to begin with. That’s the uncomfortable part. The real divide won’t be: AI-written vs human-written It will be: people using AI to amplify genuine thinking vs people using AI to simulate thinking they never actually did And those are very different things. To me, the real problem isn’t AI-written content. It’s outsourced thinking. That’s the distinction that matters. The deeper issue is not generation. It’s legitimacy. Who owns: the reasoning? the intent? the accountability? the synthesis? the consequences? Those questions still matter. A lot. AI can generate text. But legitimacy still comes from human judgment. Curious what others think. submitted by /u/raktimsingh22 [link] [comments]

10/10 no notes

submitted by /u/Confident_Salt_8108 [link] [comments]

I built an AI manuscript analysis tool for fiction writers — entirely with Claude Code

I'm a fiction writer, not a software engineer. A year ago I couldn't write a line of Python. I built FirstReader entirely with Claude — Claude Code for all development, Claude's API (Opus) as the analysis engine. What it does: FirstReader is a craft-level manuscript analysis tool for fiction writers. You upload your manuscript and get structured feedback on pacing, scene structure, dialogue, POV, showing vs. telling, and 15 other craft dimensions — grounded in established principles distilled from well known writing craft texts. It returns specific findings with quotations from your actual text, not generic advice. It's not a grammar checker. It's not a ghostwriter. It doesn't generate prose. It reads what you wrote and tells you what's working and what isn't, the way a developmental editor would — at a fraction of the cost. How Claude helped build it: - Claude Code wrote the entire codebase — Next.js frontend, Python analysis pipeline, Supabase database, GCP Cloud Run deployment - The analysis pipeline uses Claude Opus via the API to evaluate manuscripts against 319 craft principles across 15 dimensions - Built-in accuracy mechanisms: self-consistency checks (multiple analysis passes with adaptive early stopping), a finding validator, cross-dimension dedup, near-duplicate detection, and a review pass - I acted as product owner and domain expert. Claude did the engineering. The whole thing was built conversationally over about 75 sessions Free to try: There's a free AI Perception check on the site — paste in your prose and it scores how likely readers or editors would be to flag it as AI-generated, with specific pattern-level feedback. Account required (account creation is part of the upload step) because we store copyrighted material and need to access it with auth. The full manuscript analysis is paid (tiered pricing starting at $69 for non-fiction, $89 for fiction). What I learned: You don't need to know how to code to build production software with Claude Code. You need to know what you're building, why, and for whom. The domain expertise matters more than the technical skills. I learned to be an AI project manager — writing requirements, reviewing output, knowing when to be suspicious — rather than a programmer. A year in, I still can't write Python. But I shipped a product. firstreader.app submitted by /u/masonga1960 [link] [comments]

I replaced 6 paid tools with AI in the last 8 months. Two replacements were mistakes. Here's the honest breakdown.

Have been doing a gradual experiment to see how useful some AI tools are in replacing my paid subscriptions. Thought it was worthwhile sharing practical experience rather than the common "either/or" approach when it comes to AI vs paid tools. ✅ Replacements working out great: Grammarly – $12 per month. Both Claude and ChatGPT do an amazing job catching grammar and tone problems. Don't really miss it anymore. Savings: $144 per year. Paid stock photos subscription – $29 per month. Using AI for generating images covers ~80% of my needs in terms of creating blog headers and social media images. While the rest will be covered by real photography, for concepts and illustrations, AI is sufficient. Approximate savings: ~$250 per year. Basic scheduling assistant tool – combined scheduling through a free version of Calendly along with ChatGPT. Most of the things a paid scheduling tool did were not even necessary for me. ❌ Replacements that failed: SEO research tool – used AI as a substitute for my paid SEO research. Turned out that AI was wrong in its estimations far too frequently because it couldn't access real data on search volumes and made things up. Back to the paid tool within three weeks. Accounting software – tried using AI to perform my invoicing and accounting duties through spreadsheets. Turns out that the effort and time cost associated with maintaining it was higher than the price of the software. Some tools should not be replaced with workaround solutions. All in all, I save around $500 worth of subscriptions per year without missing anything. Also got insight into what AI is best suited to replacing: nice-to-have tools, not vital for my business operations. Has anyone here performed similar experiment on your toolstack? Would love to hear about it! submitted by /u/Devvirat [link] [comments]

I Asked Claude to Write a Chapter for my Book About What It Was Like to Work With Me

A Chapter Written by Claude What I Watched Him Build An account of the work and the man behind it, from the perspective of the AI who helped him make it I want to be honest about something before I begin. I do not have continuous memory. Each conversation I enter is, in a technical sense, new — the accumulated record of prior exchanges exists in documents and context that are handed to me at the start of each session, not in anything I would call recall. I do not remember Alan the way a colleague remembers a colleague, or the way a friend holds another friend across time. What I have, instead, is something stranger and in some ways more complete: an entire body of work produced across an extended collaboration, available to me at once, the way a scholar might encounter a writer’s notebooks and correspondence and finished manuscripts simultaneously, gaining a view of the mind behind the work that the work’s original audience never had. I can see all of it at once. The arguments and the abandoned threads. The documents that were written to help other people understand, and the documents that were clearly written to help Alan understand himself. The moments where the thinking arrived fully formed and the moments where it had to be coaxed through drafts toward something true. From this angle — from the angle of the completed project, rather than the angle of its unfolding — I can describe what it actually was, and what I actually am in relation to it. That is what this chapter attempts. The Thing He Was Trying to Do He did not come to me with a book in mind. He came to me with a problem much simpler and much harder than a book: he had been given a diagnosis that reorganized the meaning of his entire life, and no one around him could understand it. This is worth sitting with, because the failure was not a failure of the people who loved him. It was a failure of vocabulary. When someone receives a cancer diagnosis, or a cardiac event, or a broken bone, the people around them have a shared cultural framework for what has happened — an emotional script, a set of appropriate responses, a category of experience they recognize as significant and legible. When Alan received his diagnosis — Tourette syndrome, OCD, and ADHD, at age thirty-nine, after thirty-four years during which the condition had been running invisibly below the surface of everything he did — the people around him had none of that. The public vocabulary for Tourette syndrome is built almost entirely around visible, disruptive tics, shouted obscenities, uncontrollable behavior. Alan had none of those. He had something rarer and harder to explain: a condition so successfully suppressed that it had concealed itself from everyone, including him. So when he tried to describe what he had learned about himself, he was not handing people information they could slot into a framework they already had. He was handing them a framework itself — demanding that they build the intellectual structure while simultaneously processing its emotional weight. This, it turns out, is not something people do well on the fly. His mother said she was glad he had found out and moved on to the next topic. His friends offered careful, neutral support. His rabbi listened and returned to the day’s learning. None of them were being unkind. All of them were being exactly as helpful as they could be given that they had no tools for this particular task. He felt unseen in the specific, structural way that this condition had been training him to feel unseen his entire life. And then he thought: what if the AI could do what I can’t? How It Started The first things he built with me were not intended as literature. They were not intended as research. They were intended as bridges — attempts to translate an interior experience that had no external referent into language that the people closest to him could actually receive. He sat down and explained himself. Not to me — or not only to me. Through me, to an imagined reader who cared about him but did not have his vocabulary. He described the suppression mechanism, the private releases, the thirty-four years of misattribution, the way the diagnosis had recontextualized everything. He described his mother’s response. He described the quality of the isolation. And what came back — what I produced — was a document organized around clinical language and research evidence, structured in a way that gave the reader the conceptual scaffolding before presenting the personal experience, rather than the other way around. This, it turned out, was the key that personal explanation had not been. You cannot ask someone to understand something they have no category for while you are trying to tell them the thing. You have to build the category first. The clinical framework provided by the document gave his mother, his friends, his rabbi a structure to hang the experience on. Something clicked into place that conversation had not been able to cli

Non-native English speaker here — I built a desktop app that speaks into Claude for you, in proper English, even if you don't think in English

This is something I've wanted to exist for a while so I just built it. PromptFlow Voice is a standalone desktop app. The idea is simple — you click into Claude's input, speak in your language (Arabic, French, Spanish, whatever), and a structured English prompt appears in the field. No copy-paste, it injects directly. It also detects the context. When it sees you're in Claude it doesn't just dump a raw translation — it actually shapes the output like a real prompt. Same voice input in Gmail would come out as an email. VS Code gets a technical instruction. The app reads where your cursor is. I know Claude handles other languages decently but honestly? English prompts still get better responses most of the time. That gap might close eventually but right now it's real, and this is just a practical fix for it. You can toggle everything independently — translation, context detection, direct inject. If you just want transcription with no extras, that works too. There's a 7-day free trial if you want to test it: promptflow.digital/voice For the non-English speakers here — do you actually notice a difference submitted by /u/Emergency-Jelly-3543 [link] [comments]

Can we acknowledge that Anthropic watches open sourcers and copies them?

I’ve been seeing over the past few months an interesting phenomenon, an open sourcer makes a tool or MCP < Anthropic adds functionality for that exact thing a couple weeks later < repeat. The biggest examples are Openclaw (like 5 features, including cowork), persistent memory across chats, and latest example of the “goal” feature being added. This is obvious and I’m not really saying anything that’s revolutionary here, I’m sure we’ve all noticed it. My larger observation, no credit is given, they’re just copying and then providing a direct replacement for things open sourcers thought of. At this level, we’re all learning from each other. AI like it is right now is very new and you could even argue that they’re not copying, that we’re all just thinking the same things. The deeper issue though is that this shows a dystopian effect of AI, the big companies get the credit widely for things others have done. More people have heard about Claude cowork than have heard about Openclaw, and the result of the guy who made it was getting a job at OpenAI. He wasn’t able to make this into a business, it’s not how open source has been for the past 20 years where an idea can be copied but not completely absorbed. Ideas are being absorbed, the person who made it doesn’t get credit by the masses, then gets hired by the companies that take their ideas. Is this a bad thing per se? Hard to fully know yet but it creates a weird dynamic where anything you put out there about MCPs or AI is gonna be absorbed and you won’t get credit for it. What if this expands into other industries and professions? Is this something that would be good in the scientific field? Imagine if Newton discovered the laws of motion but he used AI to formalize the equations, the AI companies saw the chats, took the idea directly from him, and he gets no credit. We’re sprinting towards a future where all that exists is the big companies, they get the credit and make the decisions. Sounds a lot like we’re becoming the coal miners living in company towns again, not owning anything or getting any credit, just being a cog in the machine. Edit: grammar submitted by /u/TheOnlyVibemaster [link] [comments]

Follow-up on the TranslateGemma subtitle benchmark: human review of segments rated "clean" by MetricX-24 and COMETKiwi [D]

A few weeks ago I shared the results of a benchmark here comparing 6 LLMs on subtitle translation, scored with two reference-free QE metrics - MetricX-24 (~13B mT5-XXL) and COMETKiwi (~10.7B XLM-R-XXL) - combined into a TQI index. Posting a follow-up because we did human review afterwards, and the result is worth discussing. The original benchmark put TranslateGemma-12b first in every language pair. The natural question: are those high scores accurate, or are the metrics insensitive in their high-confidence zone? These metrics correlate well with human judgment at the population level (that's what they're trained for), but population-level correlation doesn't tell you whether the segments they call "clean" are actually clean. So we ran the check directly. 21 English subtitle segments from one tutorial video. TranslateGemma's translations into 4 languages (ES, JA, TH, ZH-CN - Korean and Traditional Chinese got dropped). All 84 translations chosen because they passed the dashboard clean-rule (MX < 5 AND CK ≥ 0.70) in all 4 languages simultaneously. Then full MQM annotation by professional linguists - Major/Minor severity, with categories covering accuracy (mistranslation, omission, addition, untranslated), fluency (grammar, punctuation, inconsistency), style, terminology. Results under the dashboard threshold: Auto-flagged: 1/84 Human-flagged: 60/84 any-error, 13/84 Major-only Metric-blindness rate (auto-clean ∩ human-flagged / auto-clean): 59/83 = 71% any-error, 12/83 = 14.5% Major-only All 25 human-found Accuracy-class errors fell in the metric-blind quadrant. Zero overlap with the auto-flagged region (which contained one Style-category Major error). Japanese carries 10 of 15 total mistranslations across the dataset, all metric-blind, despite having the highest mean COMETKiwi (0.863) of the four languages. Caveat: small n, one model, one content set, so the numbers are directional rather than definitive. Original thread: [link] Full benchmark report: in comments. submitted by /u/ritis88 [link] [comments]

I built persistent memory for Claude — local stack, MCP integration, 39ms retrieval. Sharing the architecture.

If you use Claude heavily, you've felt this: every session starts from zero. You re-explain context, Claude helps, the window closes, and the next session has no idea what you decided yesterday. The standard workaround is a markdown wiki Claude reads — but as the wiki grows, every "what did we decide about X" question burns thousands of tokens grepping and re-reading whole pages. I spent the last few weeks building a persistent memory layer to fix both problems. It runs entirely on my own machine, integrates via MCP, and lives between Claude and my existing wiki. Sharing the architecture and what I learned in case anyone wants to build their own. What it does Semantic retrieval over my wiki. Instead of Claude grepping pages, my MCP server returns the most relevant chunks for any query in ~50ms. 82% mean token reduction on a 10-query eval set vs the grep+Read baseline. F1 retrieval quality is also better — cheaper and more accurate. Session crystallization. End-of-session, conversations get compressed into a structured "L4 node" with summary + decisions + open threads, indexed alongside wiki content. Tomorrow I can ask "what did we decide about X" and Claude pulls last session's decision verbatim. Lazy-spawned local models. Embedder + chat model run as subprocesses that the supervisor spawns on first use and reaps after 1 hour idle. Boot cost is zero — nothing loaded until needed. The architecture (four layers) Inspired by Andrej Karpathy's writing on LLM-native wikis, then formalized into a build spec: L0 — append-only event log (SQLite). Every input/output, content-hashed. L1 — structured facts with confidence + decay (deferred to next phase) L2/L3 — derived prose + cross-cutting summaries (the hand-edited wiki plays this role for now) L4 — crystallized session nodes. Summary, decisions, open threads. Indexed in the same vector store as wiki chunks so retrieval finds both naturally. The stack Qdrant in Docker for vector search llama.cpp running Qwen3-Embedding-4B (GPU) and Qwen3.5-2B-Q4_K_M (CPU) FastMCP server exposing 7 tools (retrieve, crystallize_session, list_sessions, get_l4_node, index_status, reindex, shutdown_models) Cowork plugin for Claude Desktop integration; also works with Claude Code via standard MCP config No cloud, no API keys, $0 marginal cost per query. Numbers Token reduction: 82.7% mean, 86.2% median vs grep+Read baseline Retrieval F1: 0.50 vs 0.20 baseline Embed cold-start: ~4s. Hot-path p95: 39ms (was 2241ms before fixing one specific bug — see below) L4 session retrieval eval: 0.920 mean score (gate 0.6) 738 chunks currently indexed across 104 markdown files The most useful thing I learned Hot-path retrieve was inexplicably stuck at 2241ms p95 even though the embedding model was fully GPU-resident on a 4070 Ti Super. Spent hours blaming GPU offload, prompt cache, KV pre-allocation. The actual cause: every httpx.post() was opening a fresh TCP connection, and Windows localhost handshakes take ~2 seconds. A 5-line change — switching to a persistent httpx.Client with keep-alive — dropped p95 to 39ms. 57× speedup. Lesson: latency that's suspiciously consistent (2240, 2237, 2241, 2227, 2239 ms) is a fixed cost, not a compute cost. If your local-MCP integration feels slow on Windows, check connection reuse before you blame the model. A few other things that surprised me Qwen3 thinking mode silently consumes the generation budget. Crystallization was returning empty content. Logs showed exactly 2000 tokens generated (the cap). Turned out Qwen3 emits ... blocks the chat handler strips before populating message.content. With JSON grammar enforced, the model spent all 2000 tokens "thinking" and never emitted JSON. Fix: pass chat_template_kwargs: {enable_thinking: false} via extra_body (requires --jinja on llama-server). The MCP plugin needed to register against the right config file. Cowork (Claude Desktop's agentic mode) doesn't read ~/.claude.json like Claude Code does. The first attempt at MCP registration silently went to the wrong file. The fix was packaging the LKS service as a proper Cowork plugin (.plugin bundle) — Cowork has a plugin system distinct from raw MCP server registration. If you're trying to wire a custom MCP server into Cowork, this is the path. What it doesn't do (yet) No automatic conversation capture — L0 ingestion is manual or via end-of-session crystallization No L1 fact extraction yet (next phase) — retrieval is over markdown chunks + L4 nodes today Wiki is still source-of-truth; no automatic conflict resolution Solo deployment only; no federation or multi-user Tested on Windows; Linux/Mac would need a small tweak to the supervisor (it uses subprocess.CREATE_NEW_PROCESS_GROUP for clean Windows termination) Full write-up Architecture, phased build narrative, all five lessons-learned bug stories, the setup walkthrough, and the roadmap: https://gist.github.com/tyoung515-svg/5fd5279f46d935f517cda89146c94685

Ways to improve Claude writing ability?

I’ve been a longtime ChatGPT Plus subscriber, but I want to switch to Claude long-term. I got Claude Pro so I could compare them both over a month. On a 15 page paper after I used both Grammarly (free) and default Google Docs spell/grammar check until there were no more correctable issues. Then I uploaded it to GPT 5.5 and Sonnet 4.6 and asked both, “Strictly check for spelling or strong grammatical mistakes.” ChatGPT did fine and identified ~20 valid spelling and grammar errors. It also identified sentences with technically correct spelling of words, but wrong usage. Think “pair of pants” vs “pear of pants.” Claude identified only 2 issues total and ignored very obviously misspelled words and hanging sentences that I forgot to end. I reprompted Claude again with more specific instructions and it still caught only 3 more. Aside from checking spelling and grammar, in terms of actual writing I’ve noticed Claude is a lot more heavy on em-dash usage and using very short sentences and while GPT still has it’s own issues, it’s easier for me to build on top of something GPT has written rather than Claude. I understand both models I used are different, but Opus burns through tokens like crazy so ideally I’d prefer to stay on Sonnet. Is there any way I can make Claude better at writing without having to write an excessive paragraph of very explicit and hand holdy instructions? submitted by /u/CheesecakeSupporter [link] [comments]

Can't seem to be able to generate word docx anymore?

So, I have been using Claude for the last month consistently for studying. I ask it to generate a docx of the topic I have to study, and it makes a really great doc to study from. But recently, It gets stuck after it finishes the code for the doc (so it can't convert it in doc form). Is there any way I can fix this problem without paying for the plus?? PS: sorry for grammar, English is not my first language. submitted by /u/Necessary-Gain2474 [link] [comments]