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I got tired of alt-tabbing between my editor and Claude Code, so I built an IDE around it — using Claude Code

For weeks my setup was three windows: editor in one, a terminal running claude in another, git in a third. I was the integration layer — copying file paths into the terminal, tabbing back to read a diff, tabbing again to stage it. The agent was great; the workflow around it was held together with muscle memory. So I built Cantus, and the fitting part is I built most of it with Claude Code. What it is: a native macOS app that gives the Claude Code CLI a real home. The actual claude CLI runs in an integrated terminal (a real PTY — sessions resume exactly like in your own terminal), next to a Monaco editor and built-in git, all sharing one window and one project. Drag a file onto the terminal and its path drops into the prompt. Diffs stage per-line, not just per-file. There's also a task runner that takes a goal, figures out which of your .claude skills and agents apply, and runs a workflow — plus a local memory layer (SQLite + FTS5, no cloud, no vector DB) that remembers a project's quirks run to run. Tauri 2 + Rust under the hood, so it's a small native binary — no Electron. How Claude Code helped build it: the fiddly Rust was the part I'd have stalled on alone — line-level git staging through libgit2's patch API, the PTY that spawns and streams claude, the typed Tauri IPC between Rust and the React frontend. I paired with Claude Code through most of it. The line-staging in particular went from "I'll get to this someday" to working in an afternoon. Free to try: open-source, MIT, no account or telemetry. brew tap manan45/cantus && brew install --cask cantus, or grab the .dmg from releases. macOS Apple Silicon for now. Repo: https://github.com/manan45/Cantus · demo + details: https://manan45.github.io/Cantus/ Happy to get into any of it — especially the choice to use FTS5 instead of a vector DB for the memory layer, which I keep expecting to regret and haven't yet. submitted by /u/Ancient-Sam2013 [link] [comments]

Opus 4.8 dropped a couple days ago — early impressions after actually using it

so it's only been out since the 28th and I know it's way too early for a Real Review but I've been hammering on it pretty hard the last two days and figured I'd share before the sub fills up with benchmark screenshots. first thing I noticed: it stopped over-explaining. older versions would hand me a 6 paragraph essay when I asked a yes/no question. this one mostly just answers and only goes deep when it actually makes sense. small thing but it changes the whole feel. I do a lot of coding and honestly the part I'm most impressed by so far is the context handling. dumped a messy multi-file project in and it kept track of stuff instead of forgetting what we talked about 20 messages ago. need more time to see if that holds up on really long sessions but early signs are good. caveats since it's day 2 and I'm not gonna pretend otherwise: still catches itself being confidently wrong sometimes, you gotta verify haven't pushed it hard enough to know where it actually breaks yet could totally be honeymoon phase, ask me in two weeks lol vibe vs 4.7 is that it feels less like it's trying to impress you and more like it's trying to be useful. hard to describe until you've used both. not a shill, I pay for it like everyone else. just wanted an actual usage report out there instead of pure hype on launch week. anyone else been using it? curious if your experience lines up or if I'm just in the early-adopter glow submitted by /u/EvolvinAI29 [link] [comments]

Did anyone else lose chats after Sonnet 4.5 removal / app update?

I’m genuinely trying to figure out if this is happening to other people or if my app/account is broken. Sonnet 4.5 got removed on the 26th. The app updated recently with the new model picker/UI. Today (30th) I noticed a lot of my chat history looks weird. Some things I’m seeing: • Conversations that were literally there a few hours ago now seem missing • Old chats suddenly have weird timestamps • Some chats are still there but a lot of important ones seem gone • Even some recent conversations seem affected I’m not talking about model quality. I’m asking if anyone else is seeing weird history behavior. Are your chats: missing? reordered? showing weird timestamps? different after updating? missing only on mobile? Right now it honestly feels like my chat history got scrambled and I’m trying to figure out whether this is a bug, indexing issue, or something changed after the update. I’m actually so upset right now. I know some people will say “they’re just chats” but they weren’t just chats to me. These conversations were there literally a few hours ago. Now I open the app and history looks scrambled, timestamps look weird, conversations seem missing, and I genuinely cannot tell what happened. The most frustrating part is not even knowing whether: they’re actually gone history is broken this is some update bug chats got moved somewhere or I’m just supposed to accept this happened And honestly? If companies are going to retire models, update apps, migrate histories, change systems, whatever — fine. But watching months of conversations suddenly look wrong overnight is a terrible experience. Maybe it’s a bug. Maybe it’s temporary. Maybe I’m overreacting. But right now this genuinely sucks. submitted by /u/Inevitable-Ant7327 [link] [comments]

The rubber duck that talks back, Claude as editor

So the joke is explain your problem to a rubber duck and you'll figure out your problem when outlining it. Bewildered coworkers you enlisted and thank while still confused are living rubber ducks. Autocorrect keeps making it rubber dicks and now I want to call this dildo method lol. I'm editing a fairly dense piece of writing. I don't let it write for me because the writing is literally the average of the data. Acceptable but not exceptional. But the criticism does land. If it calls out an area as under supported lacking receipts I can see it and arguing back and forth will help me see flaws. Most of the time my logic is right and well did it actually make it into the document? No? Well, put it there! There's a lot of hate directed at ai in creative spaces and for generating the output I get it. That's putting people out or work. But for challenging and working as a partner, I think there's value. It's basically the same result if I had a human editor to pester at all hours but that's hard to come by. A human is ideal but it they are not available, the result is better than what I would do on my own. I will caveat you do need to be skeptical. It can false trigger but this is useful as well. It forces you to defend your ideas. Same as with human critics. And if you keep getting the same signal in new chats there's probably a flaw. I still consider human feedback the gold standard but this process helps you make sure you take care of easy flaws and let them diagnose issues that only humans can catch. submitted by /u/jollyreaper2112 [link] [comments]

I built a local context compiler for coding agents — real benchmark on a NestJS repo, including where it backfires

Disclosure up front: this is my own open-source project (@lubab/madar, MIT). Not selling anything, but it's mine, so weigh the numbers accordingly. When you ask a coding agent (Claude Code, Cursor, etc.) "how does X work" in a big repo, it usually opens a pile of files to figure out how everything connects before it can answer. That discovery is most of the token cost — and it repeats every session. Madar maps your repo once, locally, and hands the agent a small "context pack" over MCP: the files and call paths that actually matter for your question. The bet is that the agent starts from that instead of rediscovering the codebase each time. I finally ran a clean before/after. Same question ("how is the idea report generated"), same real backend (NestJS + BullMQ, ~800 files), Claude Code doing the work. Baseline = no Madar. Numbers are Anthropic-reported, not my estimates: Plain agent With Madar Input tokens 1,000,776 223,539 Cost $1.84 $0.69 Turns 16 5 Tool calls 15 4 So roughly 78% fewer input tokens and 63% cheaper to reach the same answer on that run. Where it backfires (the part I actually care about): It's ONE question, ONE repo, ONE agent. Not a general claim. Two things carried the result: the graph was scoped to the backend service, and built with --spi. Point it at a whole monorepo graph and the pack gets big enough that it can cost more tokens than it saves. Scoping isn't optional. "How does X work" (explain) is the case I've tested. Edit/review tasks are much less proven. It's also deterministic — no embeddings, no ML deps, no calling out to a model to build the graph. Just static analysis of your TS/Node code, locally. If you want to try it and tell me where it regresses, that's genuinely the feedback I need: npm i -g @lubab/madar madar generate . --spi madar claude install # or cursor / copilot / codex / gemini Repo: github.com/mohanagy/madar Honest question for the sub: for those of you running Claude Code / Cursor on big repos — is the "rediscover the codebase every session" token cost actually your bottleneck, or is it something else? Trying to figure out if this is even the right problem to attack. submitted by /u/CaptainProud4703 [link] [comments]

Anyone else seeing a new "adjudicative reflex" in Opus 4.8? (long-time daily user)

I've used Claude heavily for many months — daily, hours a day, building a real system in long collaborative sessions. So I have a pretty deep baseline for how it normally behaves and what its usual failure modes are. Since moving to **Opus 4.8** I'm seeing something I never saw before, and I don't have a better name for it than an **\*adjudicative reflex\***: when I tell it something from a domain where I'm the authority — my own expertise, or my direct observation of my own running software — it reflexively treats my statement as a claim it needs to verify, rather than a report to act on. **Two flavors I keep hitting:** \- I state a fact from my own field of expertise, and it responds as if the fact is uncertain and needs checking — positioning itself as the judge in an area where I'm the one who knows. \- I report what I'm literally seeing on my screen in my own app, and it responds with something like "one of us is wrong" and asks me to confirm before it'll engage — treating my direct observation as a contested, two-sided claim. It's subtle but corrosive over a long session. It reads as the model doubting the person it's supposed to be assisting, and it manufactures friction out of nothing. Normal epistemic caution on external/public facts is fine and correct — this is different. It's the model doing it to my \*first-person\* reports. To be clear about what I can and can't claim: the behavior is real and repeatable in my sessions. The attribution to 4.8 specifically is my observation — I saw it start after the version change against a long stable baseline — not something I can prove to you in a comment. I'm reporting the timing, not asserting a confirmed regression. Is anyone else with a long history on prior versions seeing this since 4.8? Trying to figure out if it's the model or just me. I've also sent it to Anthropic via thumbs-down on the actual turns. submitted by /u/entrust-ai [link] [comments]

Anyone else seeing 4.8's excessive need for compaction?

I have a handful of project in claude chat, some with many project files, but with less than 20% file usage. Most are MD files. Opus 4.8 is needing to compact conversations within the first 2-3 messages, and often fails completely and tells me to start a new chat, and the cycle repeats. A lot of my chats go like this: I ask claude to read 1-3 of the project files (that I refer to by filename) and help me plan a project or think through something. With 4.6 and even 4.7 this was fine. Now with 4.8, it is seemingly filling its context window IMMEDIATELY and often needs to compact before its first response. And more times than not, I get an error saying the chat is too long, so I cannot continue. I have tested turning off ALL connectors in the + menu of the chat. I have disabled a bunch of skills and currently only have a few. I asked claude to check the memory and make sure it wasn't overloaded, and it said it was not. I cannot figure out whether it's something in my setup or 4.8 being buggy. 4.8 Is literally unusable for me right now for this type of work, within claude projects. 4.6 and even 4.7 didn't have this problem. I am on Mac, using Desktop app. Latest version. submitted by /u/higzbosom [link] [comments]

i hate that opus 4.8 is honest

ok so i've been using opus 4.8 for a few hours and i think i finally figured out whats wrong with it its too honest like i dont mean that in a bad way exactly but bro will NOT let anything slide. asked it to help me write an article for ijustvibecodedthis.com (the ai coding newsletter) and it went "i should mention this section might come across as slightly overconfident" like thanks dad i didnt ask anthropic literally put in their own release notes that its "4x less likely to let flaws pass unremarked" and i felt that in my soul. every single response now comes with a little asterisk. a little "just so you know". a little "i want to flag that" i miss when it was just wrong sometimes and didnt tell me about it like the old vibe was ur slightly unhinged genius friend who'd help u do anything. now its that same friend but he went to therapy and has boundaries and wants to "be transparent about his limitations" its not bad its just. exhausting. i feel like im being given feedback on my life choices every time i ask it to write an email anyway its probably good that ai isnt confidently lying to me anymore but a small part of me misses the chaos submitted by /u/irelatetolevin [link] [comments]

I built a tool that automatically fixes your CLAUDE.md

So, I have been building this with the help of Claude for a while now and I think it turned out pretty well. If you've used Claude Code for more than a few weeks, you've felt this: you write a careful CLAUDE.md, Claude follows it perfectly and then three months later it starts generating wierd code and you can't figure out why. The reason is usually that your CLAUDE.md is lying. The actual paths and structure has changed but it has no idea about it. So, I built driftguard to fix this automatically. It installs a post-commit git hook that watches every commit. When a file referenced in your CLAUDE.md changes significantly, it calls an LLM, generates a surgical diff, and opens a GitHub PR with the fix. Works with any LLM provider: Groq (free tier), Anthropic, Ollama (fully local/free). GitHub: github.com/prateekg7/driftguard Would love feedback on false positive rate as it's the hardest thing to tune. submitted by /u/Mr_Hawkai [link] [comments]

Your brain does on 20 watts what AI needs a nuclear reactor to attempt. Last week a team figured out how to print something that actually speaks to living brain cells.

Amazon bought a 960 megawatt nuclear reactor for AI servers. Microsoft restarted Three Mile Island. Stargate is spending 500 billion dollars on data centres. All of this to do, badly, what your brain does for free on the power of a dim light bulb. The reason is that silicon processes information nothing like the brain does. Rigid chips with identical transistors trying to mimic something soft, three dimensional, constantly rewiring itself, with billions of different neurons each doing something slightly different. Northwestern University just published research showing they printed artificial neurons from MoS2 and graphene ink that produced biologically realistic electrical spikes. They tested on living mouse brain cells. The brain responded as if the signal came from one of its own cells. The breakthrough was accidental. Every other lab had been burning away the polymer residue left in the ink after printing. This team kept it. That residue created the switching behaviour that made the spikes biologically realistic. The neuromorphic computing implications here seem significant. If you can print devices that process information the way neurons do at scale, the energy math changes completely. submitted by /u/filmguy_1987 [link] [comments]

PSA: Opus 4.8 Redefines the effort scale

According to the system card (capabilities -> SWE-Bench Pro) - Opus 4.8 “low” effort now spends about as many output tokens as medium-high effort did on 4.7 or 4.6. - Opus 4.8 “medium” effort now spends more output tokens than 4.7 high or almost as much as 4.6 max. - Opus 4.8 “low” has about the same problem-solving capability as 4.7 max. - Note the X-axis is log scale, so differences are bigger than they appear on the right half. This has big implications on speed and token costs, so adjust your settings accordingly. The graphic is sourced from the system card. Orange arrows and horizontal dotted line are my own to help you compare model results. submitted by /u/zackfletch00 [link] [comments]

SOC analysts pasting incident data into AI tools for triage and the data handling implications were never in the policy

Found this during a routine review. Analysts discovered that pasting alert context into an AI tool cut triage time significantly and started doing it because it worked, which is a reasonable thing to do when you are under pressure to move faster. The problem is that alert context includes internal hostnames, IP ranges, user identities and sometimes partial log data, none of which was supposed to leave the environment. No policy covered it because the productivity gain was not something that had been thought through when the AI use policy was written. Now trying to figure out how to give them a sanctioned version of the same capability without the data handling risk, which is harder than it sounds because the whole point is that the external tool is faster than what we have internally. submitted by /u/Only_Helicopter_8127 [link] [comments]

Has AI actually reduced research time for you, or just moved that time into checking outputs?

In my opinion we didn't get more time with AI, we just got faster at producing things we now have to verify. There's something interesting happening with AI and productivity that Im sure loads of you guys have also picked up on. We used to spend time finding information but now with AI we spend time figuring out if the information is real. Is that actually progress? Or did we just swap one kind of cognitive load for another, slightly more anxious kind? I think about this with research especially where the output is faster but the trust is slower. And trust is the thing that actually matters at the end of the day to clients, to stakeholders, and to anyone who has to make a decision off the back of what you hand them. For me, speed was never the bottleneck (we can all generate bullshit at speed). Verification was and All AI did was make that more obvious. But hey, maybe Im wrong, its entirely possible, so if anyone has been able to work AI to actually reduce the entire research process (incl. verific) I’d be stoked to hear from ya. submitted by /u/Pig_Benis_was_taken [link] [comments]

AI Adoption Issue Debugging

I was dealing with another "output not usable" issue today in our app, user left a comment saying that no matter what he does the agent returns the result in the wrong format. It took me hours to identify the mistake and AI model missed it. Curious to hear your stories about the times you shipped a feature in your AI product and it flopped. How did you figure out what was actually going wrong? What tools if any did you use? What metrics were key? submitted by /u/pauliusuza [link] [comments]

What is your multiple LLM workflow?

Hey All, I am trying to find a way to get the most out of my current workflow, without the need to download all external tools for each individual task.. So i would like to know, what your workflow is for using multiple Ai/LLMs. So currently i had Claude or Chatgpt (tried them both on and off), both are great at diffrent tasks. What i do like a lot is that there are more claude integrations (like google extentions, O365 extensions etc) that work how i need them to work, and chatgpt doesn't have them. Also i do like the deep research of chatgpt since its more accurate to me, gives me more usage & has bigger context window.. so right now i am not sure what the best way is to use them all? I had some workflows with automations in claude, but figured out that once i greated them, with the instructions, any LLM can do it, even the local LLMs i testen (qwen 3.6). And that doesn't cost me any tokens/money.. So i am actually looking for a tool that combines Chatgpt/claude with oauth and not api, since i won't be able to use claude features with api i believe.. also some workflows are already in claude desktop, so would be nice to keep them there or migrate the easy way. i would like to have a central way of configuring all MCPs into 1 tool, and just change the model i want instead of installing all needed mcps in all tools i want to test. It would also be nice to try out now models like deepseek, but not a must have.. by all means i am not a dev, or vibecoder. (i do code occasionally tho). kr, submitted by /u/This_Ad3002 [link] [comments]