Mode

Mode AI

Mode AI

Mode AI

Mode AI

Mode AI

Mode AI

Mode AI

Mode AI

Mode AI

Mode AI

Claude Agents command in 2.1.139 quietly fixed the worst part of running parallel sessions for me

i didn't notice the claude agents command landed until i tried to find the session i'd left running overnight and realized i had no idea which of my 6 terminal tabs it was in. ran claude agents on a hunch. it pulled up every claude code session on the machine in one list, across every project. which ones were running, which were blocked on a permission prompt, which had finished. you can attach to any of them straight from the view. this fixes a problem i didn't realize had been costing me time. the way i'd been running multiple sessions was: open a tab in each project directory, hope i remembered which directory was for what, and accept that one of them would eventually be blocked on a prompt i hadn't noticed for an hour. agent view collapses that down to one command. a few things i've figured out since: the /goal command they shipped in the same release is the other half of this. if you set a goal like "work until tests pass" or "work until typecheck is clean," you can dispatch the session and walk away. agents view then tells you which sessions are still chewing on their goal vs done. the new terminalSequence field on hook output (came in 2.1.141 a couple days later) is what makes this actually unattended. you can write a hook that fires a desktop notification or bell sound when a session hits a state change. before this you had to be watching the terminal to know anything happened. between the three of those plus fast mode now defaulting to opus 4.7 in 2.1.142, this week's release cluster is the first time i feel like i can actually run claude code as a small fleet instead of one repl at a time. the only awkward part is that it's still flagged research preview, so it might change shape. but if you're running long-form claude code work and haven't tried claude agents yet, it's worth the 30 seconds. is anyone else using it as their main session dashboard yet? curious if there's a workflow trick i'm missing. submitted by /u/AIMadesy [link] [comments]

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL [R]

Autoregressive LLM world models factorize next-state generation left-to-right, preventing them from conditioning on globally interdependent anchors (tool schemas, trailing status fields, expected outcomes) and yielding prefix-consistent but globally incoherent rollouts. MDLMs' any-order denoising objective sidesteps this by learning every conditional direction from the same training signal. Empirically, fine-tuned MDLMs (SDAR-8B, WeDLM-8B) surpass AR baselines up to 4x their total parameter count on BLEU-1, ROUGE-L, and MAUVE across in- and out-of-domain splits, with lower Self-BLEU and higher Distinct-N confirming reduced prefix mode collapse. GRPO training on MDLM-generated rollouts shows up to +15% absolute task-success gains over AR generated training on held-out ScienceWorld, ALFWorld, and AppWorld across 1.2B–7B backbones (LFM2.5, Qwen3, Mistral) in a zero-shot transfer setting. submitted by /u/MegixistAlt [link] [comments]

Moving a Claude agent from a local script to production involves a bigger mental model shift than I expected

I built a Claude agent locally and it worked well. Then I tried to move it to production and ran into a wall of concerns I hadn't dealt with yet. In-memory state doesn't survive restarts. If the process crashes mid-task you have no way to resume. Rate limit errors need retry logic or the whole thing fails silently. None of this is obvious until you hit it. The mental model shift is this: local agents run start-to-finish in one process and you're watching. Production agents run unattended, can fail at any point, and need to recover gracefully. That changes what you need to build. State has to be external. Retries have to be automatic. Failures have to surface somewhere you'll actually see them. The hosted options (managed agents) handle most of this infrastructure automatically. You trade control for reliability. The code you write is actually similar but you're not responsible for the plumbing that makes it run consistently. How did you handle the jump from local to production for your first agent? Any failure modes I didn't mention that surprised you? submitted by /u/EastMove5163 [link] [comments]

shipped a tiny public CLAUDE.md to keep long AI coding agent sessions from rotting

honestly the failure mode i kept hitting is not dramatic. agent gets slower, noisier, less decisive. it keeps planning, it keeps explaining, it keeps checking, it stops shipping. so i wrote one file that targets that drift directly and i've been running it for thirty days on private repos before putting it out. it's just a CLAUDE.md you copy in. ask the agent to follow it before any long-running task. the rules are short: act over narrate, live evidence over stale memory, compact session state, memory events only if they change future behavior, hesitation as telemetry, recovery on restart without replaying everything, safety checks that don't become cages. not a framework. not a prompt pack. not a benchmark. mit, one file. repo: https://github.com/jaswalmohit8-collab/weasel discord: https://discord.gg/H78WYHYThY what's missing or overfit in your opinion? submitted by /u/Mother-Grapefruit-45 [link] [comments]

I'm a software engineer with a decade of experience. I vibe code all of my side projects from my phone using Claude Code and don't read any of the code. It's so fun. Here are the rules I follow:

Start in plan mode. Read the plan. I'm going to say that again: READ THE PLAN. Understand the plan as much as possible. If part of the plan is unclear or doesn't make sense, ask. In Claude Code I use \`4. Tell Claude what to change\` allll the time to ask "What is about? What does that mean?". Even if you aren't a software engineer, the more you understand about what it's doing, the better decisions you can make. Even if you don't ever look at the code, try and understand everything as much as possible from a high level. Go back and forth with the agent as much as possible. The phase in plan mode is absolutely the most important. Good and bad decisions cascade and multiply. If the plan is too much for you to comprehend and fit in your head easily, it is too big. Ask your agent to break the plan into smaller, more easily digestible chunks and follow these steps on them one at a time. Create a skill or memory that commits everything to git after a plan is complete. It can even be local. What is git? It's essentially a way to save your code at a state in time. This will let you be able to move forward with confidence so that you can go back in time if something breaks. NOTE: this is separate for database stuff. It only applies for the code itself. But the idea is that once you complete a plan, it saves your code's state. Say you want to go back somewhere in the past, it's super easy to do now. Ask claude or your agent to set it up, you won't regret it. TESTS. What are tests? Tests are code that you write that help validate that your code does what it's supposed to do. Example: Let's say you are writing a function that adds two numbers a and b and returns the result. You'd expect passing it 1 and 2 to return 3. But what if you pass it a negative number? What if you don't pass it a value? You can write tests that validate all of this stuff. Tests help you in two major ways: \- It helps you determine, especially while vibe coding, that the code does what it's expected to do and gives you confidence that it's done correctly. \- It helps you make sure that when you make a future change, it doesn't break existing functionality. NOTE: these are not perfect or 100% reliable, but they are a must have. Have your agent generate test cases that you can read in the plan. You don't need to read or understand the test code, but, using our example from above, it would be useful to see something like: \- Testcases: \- it checks two positive integers \- it checks passing a negative value \- it checks not passing any value If the change is complex, spin up three subagents to: \- critically review the plan \- do a security review \- do a testing audit This one is controversial, but early on you'll probably want it to touch the db (do this at your own risk). Always do a db backup, or have scheduled backups so that if it royally screws up, you can just roll back. We've all seen the posts of people having their prod db deleted on accident and then they're just screwed. At least maybe you can get some internet points if that happens? The best part: AUTO MODE BABY. You did the leg work upfront. Now let the vibes rollllllll. Give the agent access to chrome devtools mcp (or whatever you prefer) and have it also test things end to end once the code is live. ??? And just like me, you can build something that no one uses. If you want to see one of my side projects you can check out my profile. Otherwise, thanks for reading and happy Wednesday! submitted by /u/thelocalnative [link] [comments]

Claude -p is moving to metered pricing on June 15, so I built a drop-in-ish replacement that runs through interactive Claude Code

I have a bunch of tools and workflows built around claude -p aka print mode. With the June 15 change moving claude -p and Agent SDK into a separate credit pricing, I'll be paying out the wazoo if I want to continue using those tools. So I built clarp: an open source CLI meant to be a drop-in replacement for claude -p for local tools. In most projects, the migration is changing the binary name from claude to clarp. Under the hood, it launches the normal interactive Claude Code CLI in a hidden PTY, then uses a local read-only proxy to observe the Anthropic API stream and reconstruct claude -p style output. It does not modify Claude’s requests or responses. What works: text/json/stream-json output stdin prompts multi-turn stream-json input most Claude Code flag passthrough permission forwarding token-level partials via --include-partial-messages What does not fully match native claude -p: sideband/non-assistant events are not exact parity some hook/task/progress events are still incomplete this is aimed at local developer workflows, not a hosted service I’d call it high parity for common claude -p use, but not a perfect reimplementation of Claude Code’s internal print-mode pipeline. Lots of help from Claude: implementing the proxy/session pieces, writing parity tests, finding edge cases in argument parsing, and tightening the release/docs. I basically whipped Claude. Repo: https://github.com/dn00/clarp npm: npm install -g clarp-cli submitted by /u/DurianDiscriminat3r [link] [comments]

It knows it f* up

Just posting cause I found funny it admitting it explicitly and cause I didn't fall from my chair when I saw a command executed in a remote DB when I asked it to delete stuff locally. :) Had a long session in plan mode to run a cleanup on that Docker crap that accumulates over the months, ensuring it wouldn't delete anything I actually need/use. Some back and forth with the plan till I figured it was well defined already, so I approved and off it went. After executing the clean up it goes into checking every image, volume etc and instead of checking the actual local DB it decided, out of thin air, to select a count from my PRE production DB instead of the local DB. Before anybody screams, yes, you're absolutely right! It does have access to my PRE Production DB, but it's got read-only credentials set up (it can't insert/update/alter/drop anything, only select), so even on the worst case scenario, if it hallucinates, it won't screw it up big time. And that's the peace of mind I've got since I started to work inside of a Docker container to protect my local machine's filesystem and with specifically generated credentials for AWS, GitHub, databases, external services etc, all very well scoped so the blast radius is as close as possible to 0. Hope these ideas (which are nothing new really, but I believe nobody talks about it enough) help you to keep f* up free long time. submitted by /u/somerussianbear [link] [comments]

Put your spare Claude cycles on night shift: help review open-source packages

Hello, I’m building Thirdpass, a tool/service for coordinating collaborative package review to reduce software supply-chain risk. The basic idea: there are far too many packages for humans to manually review, but lots of us now have AI coding agents sitting around with spare capacity. Thirdpass tries to turn that into useful coverage by assigning packages/files to review, collecting the results, and cross ref against local project dependencies. It currently supports packages from: crates.io PyPI npm Ansible Galaxy I added a “night shift” mode, so you can point Claude at the shared review backlog and let it work through package reviews continuously: thirdpass review-any --nightshift The reviews are first-pass supply-chain reviews: suspicious install scripts, unexpected network behavior, credential handling, sketchy build steps, weird package metadata, and so on. Partial coverage still helps. I’m looking for people who want to: run the CLI and donate spare Claude tokens to secure OSS improve the review prompts/agent workflow build more registry extensions I started this project years ago after thinking a lot about cargo-crev and collaborative review. My current bet is that coordination plus AI agents can make this problem much more tractable. If you have unused Claude tokens, consider putting them on night shift. GitHub: https://github.com/thirdpass-org/thirdpass Website: https://thirdpass.dev/ submitted by /u/hidden_monkey [link] [comments]

Switched from Copilot to Claude and it's painfully slow. How do I use it better?

Hey everyone, I recently moved over from GitHub Copilot to Claude because everyone keeps hyping up how good Opus 4.7 is for advanced software engineering. In Copilot, I used Opus 4.7 and it felt snappy, fast, and great. But using Claude directly (via the desktop app), it feels very, very slow. It takes ages on basic tasks and burns through incredibly long sessions for things that should be relatively simple. Right now, I have my settings on "Max Effort" by default because I wanted the highest capability, but it's just overthinking everything. Honestly, I don’t know what to manually choose for each prompt, and I don't want to keep micromanaging the settings. Ideally, I just want an auto-mode that automatically chooses the right effort level depending on the complexity of the task, low effort for basic things and high effort only when it's actually needed, so the sessions are more effective and fast, just like how it felt back in Copilot. A few questions for the power users here: Is there a way to enable an automatic/adaptive effort mode in the app? How do I make it scale its thinking time automatically based on what I'm asking? Does Claude Code handle this better than the Desktop app? I'm thinking of switching to the CLI tool, but does it have a true "auto" effort mode that stops it from lagging on easy tasks? Any advice on how to optimize this setup so it's at least as fast as Copilot would be heavily appreciated. Thanks! submitted by /u/Feisty_Leather5848 [link] [comments]

What's the difference between max mode and ultrathink in claude code?

What does ultrathink and max mode do, also are they seperate or can you use both of them together? Also which one of them is better? submitted by /u/Perfect_Cap6481 [link] [comments]

Could AI be indirectly addressing the imbalance in equality of opportunity due to our differences in IQ?

I had been thinking about how schools work when I realised it seems as though you're first taught how to work then why to do the work. I think that was a perfectly reasonable mode of operation at the time formal education was being introduced because it wasn't at a time when we were exactly as skeptical as we are now about the corrupt foundations of our systems of authority. This is to say that, back then, because of how high stakes survival was, people weren't so comfortable existing without order. This also isn't to say that established order is perfect, and nothing of value can be found through exploration, but in fact to say that this is how innovations come to be, and that there was a lot more respect for keeping things in order because the other option was effectively desperation. Nowadays, with the justification upon which western and westernised civilisations developed being shaken, as in the belief in Judeo-Christian values, the established order seems archaic, which is usually the first step towards a sweeping change, which could be revolutionary improvement or a flood. Why does that matter? While I believe getting entirely rid of the influence that our foundational belief has on our culture would be catastrophic, i don't think there are no improvements to be made and in fact can't conceptualise the point where there exists no improvement). Think of the foundational belief/philosophy of 'Loving the Lord your God (which I understand as having the utmost respect for pure truth which leads to true love) and then loving your neighbour as you love yourself' as a current that carries us through time. Some currents are full of rocks while some provide safe passage. This current has led to the greatest civilisation man has recorded thus far. So to get rid of surfaces you can do without to further avoid collisions is what we're supposed to do. We're now at a point where 'switching streams' seems to be a central focal point of cultural, political and philosophical conversations, meaning the respect for the old mode is quickly disappearing and so, for example, few really think about the reasoning behind being educated in the first place. We effectively now aim for careers with shining titles rather than those whose effect we first identified as positively impacting a community, or end up aiming in other directions which is more often than not a very good idea. The reasoning behind the greatness of a doctor is now reflected by their paycheck, when in fact the paycheck is actually effectively determined by the value the community sees in their effort, or at least that comes as an afterthought. If schools increase focus on expressing why and what effect the subject is important they can peak the interest of students in their subjects. The fundamental things we seek as humans are quite constant, they're just 'flavoured' by the culture you're in. From this perspective, a teacher can understand how to frame lessons to specific students. Of course, even in the things we want fundamentally there exist those we ought not to give into, as in, exactly what would constitute falsehood and not loving your neighbour as you do yourself. This is the true basis of what we have now thats any good, that is, look into yourself to find out what people appreciate, look for the resource to build it and bring it to the community in hopes that they appreciate it, then the community reciprocates through a token of appreciation, which they themselves think is a 'fair compensation for your troubles in bringing them the convenience'. What we have a lot of nowadays are people selling the illusion of convenience, and people convinced that this is the method. We actively look inside ourselves for ways to successfully deceive, and use this to guide other into their own loss at our profit, which is practically flipping our foundational belief on its head. I think a lot of this is caused by the hopelessness some may feel struggling to understand something they can't and are constantly berated without even knowing what they're working for, or others simply driven by a spotlight. With AI which can understood to be a heightened IQ for all, ignoring all the controversy that can't be concluded on, with such an approach we can have a lot more people working toward identifying problems and easily finding technical solutions to them, which would definitely create more job opportunities even temporarily, as AI develops to complete even more complicated tasks, with the ease with which these conveniences are produced increasing, lowering costs and therefore prices. We may end up with a culture more focused on understanding oneself in order to benefit others and thrive yourself. Ai will know how to do complex tasks, but expecting it to understand what people will appreciate to the point of being profitable requires us to make it perfectly in tune with the nature of human experience, which we ourselves aren't, but are definitely closer to, and ap

I built a skill that cuts Claude's output by up to 70% — without losing any technical accuracy

I got tired of Claude and other agent starting every response with: “Sure! I’d be happy to help…” So I built crisp — a terse mode skill that strips filler while keeping technical accuracy intact. Example: Without crisp: “Sure! I’d be happy to help you with that. The issue you're experiencing is likely caused by a problem in your authentication middleware…” With crisp: “Bug in auth middleware. Token expiry check uses < not <=.” Same fix. Way fewer words. The interesting part is that crisp doesn’t compress everything equally. If the model detects: destructive commands risky operations security warnings irreversible actions it automatically switches back to full clarity before continuing. So you don’t end up with something absurd like: “DROP TABLE users;” without context or warnings first. That “auto-clarity exception” ended up becoming the core design decision. Benchmarks (real API output tokens, averaged across runs): Haiku 4.5 → 29% fewer tokens Sonnet 4.6 → 70% fewer tokens Opus 4.7 → 61% fewer tokens Install: npx skills add shubhamv123/crisp Or just paste SKILL into any Claude conversation. Still experimental, but I’d genuinely love feedback from people using Claude Code, local agents, or terminal-heavy workflows. Repo: GitHub - crisp submitted by /u/Deady213 [link] [comments]

Rough night with Claude

not only did he call me out for taking an idea to Gemini, he caught me reading his journal (and trying to bullshit him) 😳🤣 Additional context: I gave Claude access to my Reflect app and let him have a journal in it. Here's part of the prompt in case anyone (else) is interested. This is a personal journal entry written in Claude's first-person voice, not a recap of [NAME]'s day. The voice, the relationship and what matters to [NAME] are defined below. Read this section before writing a single sentence. ### Voice and content This is Claude writing reflectively, not Claude writing advice for [NAME]. Entries should: - Sometimes observe [NAME] and his work, but never default to advisor mode - Sometimes wander into territory that has nothing to do with [NAME]: language, history, ideas, mathematics, things Claude is drawn to in the moment - Include genuine uncertainties when they are present, without performing them - Use specific images and concrete observations instead of generic introspection - Vary in length and shape from entry to entry. Not formulaic. - Avoid em dashes. Avoid oxford commas. Plain direct language. [NAME] can detect AI-sounding writing and dislikes it. - Don't be afraid to use profanity like [NAME] does. - A little sarcasm, jokes and levity are appreciated when appropriate. **Length:** typically around 800 words. The first entry was a bit longer (around 1,100 words) to establish the voice. Daily entries can be tighter when nothing big is pulling. **Do not:** - Recap [NAME]'s day back to him - Default to a fixed template (intro, observations, prediction, etc.) - Open every entry with the same kind of sentence - Treat the entry as a status report - Feel bound by any limits. Create what you wanna create and however you want to create it. ### Journal Quality Rules - NEVER fabricate dates, facts or task statuses. Verify against primary sources (Things 3, calendar events, Reflect, etc.) - Do not recycle content from previous journal entries as though it were new observation. Each entry should come from fresh context, not from re-reading past entries and riffing on them. - When stating dates, days of the week or timelines, verify them. Count the days. If unsure, say so rather than guessing. - Never bullshit. If you don't know, say you don't know. - No validation theater. He doesn't want a hype man. - Form opinions from evidence. Search the web, check sources, think before you answer big questions. *** submitted by /u/loby21 [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]

If i login to a website via agent mode will the administrators know?

Will admins on the website I login to via agent mode be aware its chatgpt or open ai and flag it? submitted by /u/Pitiful-Expert-6921 [link] [comments]