Second

Second AI

Second AI

Second AI

Second AI

Second AI

Second AI

Second AI

Second AI

Second AI

Second AI

If you run multiple AI sessions, what do you find yourself manually carrying between them?

I've been paying attention to my own workflow lately and noticed a lot of my time goes into moving stuff between AI sessions, not the actual thinking. Like I'll get an output in one session and then manually bring the relevant pieces into another so it has what it needs. What I can't tell is how much of that is necessary vs. me just being sloppy. So I'm curious how others handle it: When you move from one session to another, what do you actually carry over? Just the output, or also the reasoning, the decisions, the constraints, what to avoid? Have you ever handed off too little and the second session went sideways? Or too much and it got lost in the noise? Does anyone have a mental rule for what's "enough context" to pass along? Trying to figure out if there's a clean pattern here or if it's just inherently messy. Curious what people have landed on. submitted by /u/riley_kim [link] [comments]

Claude Code keeps looping on my fixes

I watched Claude re-suggest a fix I just undid. It happened three times in a row. The session hit the token ceiling and the assistant started hallucinating earlier edits. I was burning $1,400 in surprise bills while chasing a ghost line. I measured the impact on a real 87-file repo. Raw token count: 163,122. With the new layer it dropped to 17,722. That is an 89.1% reduction. The assistant only rereads the files it actually touched. I get 6.4x fewer tokens than reading the relevant files. In the best case I see 155x fewer tokens than pulling the whole codebase. The fix is a context layer that wraps any coding agent. It builds a bi-temporal index, auto-captures revert commits, and injects PreToolUse hooks on Edit, Write, Bash. Six Sentinel hooks install by default. The layer lives locally, zero cloud calls, SQLite backing. I added it to Claude Code via npx engramx@4.0.0. The install ran in seconds. My session stayed under the limit for the next eight hours. No more repeated suggestions. No more surprise bills. Try it. Tell me what breaks. Apache 2.0. Local. Free. submitted by /u/SearchFlashy9801 [link] [comments]

OpenAI Seemingly Not Charging For API Calls?

I've recently swapped out a Gemini api key with one from OpenAI in a purpose built app I use everyday for work. The AI's job in the app is pretty much just transcript analysis and content generation so granted, these already weren't huge calls being made. Each run within the app was averaging about $0.15 with 5.4mini. So I decided to take the load off of my computer and replace the local models I was using for a couple bots I have running, with another key from the same account. This second one makes 19 calls a day but I haven't been able to track its spend because since deploying, my balance hasn't dropped a cent. Both my bots and apps are working fine and have been for about a week now, without being charged. I'm checking my platform dashboard several times a day and while the balance hasn't dropped at all, tracking for daily token usage for both keys seems spot on. Any ideas as to what's going on here? submitted by /u/MikeNiceAtl [link] [comments]

I stopped using Claude in the browser for 80% of my daily tasks and my usage actually went up

This is going to sound counterintuitive but let me explain. I love Claude. I use Opus for deep work, Sonnet for quick stuff. I was probably using claude 15 to 20 times a day. Summaries, brainstorming, code review, email drafts, research questions. Standard knowledge worker usage. But I noticed a pattern. Most of my usage happened in bursts. I would open Claude, do 4 or 5 things, then close it and not come back for 3 hours. Not because I did not need it, but because I forgot about it. I was deep in something else and the thought "I should ask Claude about this" did not occur to me in the moment. So I built a small thing. An agent that runs Claude Sonnet on the backend, connected to my calendar, todoist, email, and a few notion databases. It lives as a contact in my iMessage called "C" (very creative I know). Now instead of opening claude when I remember to, I text C throughout the day the same way I text anyone else. "What is on my calendar after 3pm." "Draft a reply to that email from alex, keep it short, say yes to the timeline." "Remind me to review the pitch deck before tomorrow's call." "What did I write in my product notes last week about the onboarding flow." My actual Claude usage went UP significantly. Not because the model got better but because the access point changed. Texting is a zero-friction action I already do 80 times a day. Opening a browser tab is a deliberate decision I have to remember to make. The deep work still happens in claude.ai. When I need the full context window, artifacts, file uploads, the browser is still better. But that is maybe 20% of my interactions. The other 80% are quick, context-specific queries that take 30 seconds and are perfectly suited to a text message. Stack: claude sonnet via API, a small express server for the tool integrations (google calendar, todoist, notion, gmail), photon codes for iMessage delivery, deployed on a $7 render instance. Langfuse for tracing when something goes weird. Total cost is about $35 a month in API calls which is less than what I was already spending on the Pro subscription that I still also have. The meta point: Claude is incredible. The browser is holding it back for most daily use cases. Not because the browser is bad but because it requires intent. The best AI interactions are the ones that happen when you barely think about it. submitted by /u/ScaryAd2555 [link] [comments]

Claude Code changed how I think about dev workspaces

I’ve been using Claude Code more as part of normal coding sessions, and it made me rethink something pretty basic: the terminal is starting to feel too small for the kind of work these tools do. Not because Claude Code is bad in the terminal. It actually works well there. But the session around it grows quickly. You have Claude working through changes, a dev server running, logs somewhere else, maybe docs open, maybe a browser preview, maybe a second branch or worktree because you don’t fully trust the first path yet. At that point the problem is not only “what should I ask Claude?” It becomes: where does this whole working state live? I’m working on an open-source project around this idea called Cate. It’s basically a canvas workspace for terminals, editors, browser previews, and longer coding sessions. Not meant to replace Claude Code, more like a different surface around it. Free to use, open source: https://github.com/0-AI-UG/cate Curious how others here are handling this. Do you mostly keep Claude Code in one terminal, or are you already using split panes, tmux, multiple windows, worktrees, or several Claude sessions in parallel? submitted by /u/Ill_Particular_3385 [link] [comments]

I built a system that makes Claude actually remember me across sessions — here's how it works

Every time I opened a new Claude chat I had to explain myself from scratch. Who I am, what I'm working on, who the people in my life are, how I write. It got old. So I built a folder of plain text files. One about me, one for each person I deal with regularly, one per project, and a running log of decisions I've made and why. At the top there's a single file that tells Claude what to read before it does anything else. That's the entire system. No app, no database, no plugin. Now I open a chat and it already knows me. I can say "draft a follow-up to Barry" and it pulls who Barry is, the last few things we talked about, and the way I actually write, without me feeding it anything. I know the obvious reaction is "this is just ChatGPT memory" or "mem0" or "a vector DB with extra steps." It genuinely isn't, and the differences are the whole point: Nothing gets auto-captured. ChatGPT's memory decides for you what's worth keeping, and you end up with a black box you can't inspect. Mine is the reverse. I decide what goes in, so there's no junk, and I can open any file and see exactly what the model knows about me. It's text in git. I can read it, edit it, or delete a wrong fact in about two seconds. It reads, it doesn't retrieve. No embeddings, no similarity search trying to guess which chunk is relevant. The rulebook defines a fixed read order and the model loads the actual files at session start. For one person's worth of context this beat RAG every time I tried it, because RAG kept surfacing the wrong note or missing the obvious one. It outlives the tool. Plain text works with whatever model I switch to next year. No lock-in. On evidence, since fair question: I've run it as my daily driver for a few months. The concrete win is that it drafts emails in my voice that I send with little or no editing, because it has my past messages and my style notes already loaded. The video has three demos of things a cold session flat-out can't do, so you can judge for yourself rather than take my word. Limitations, because they're real: It doesn't scale to a huge corpus. Loading files into context has a ceiling, so this is built for "everything important about one person's working life," not a 10,000-note archive. If your goal is a giant searchable knowledge base, you want retrieval, not this. There's no automatic capture. If I don't write a fact down, it doesn't exist. That's the price of having no noise. Bad taxonomy degrades it quietly. What's stable versus what changes weekly, what lives in the always-read file versus what only gets opened when relevant. Get that split wrong and recall gets worse without you noticing. The code was an afternoon. Figuring out the taxonomy took weeks of actually using it. Short walkthrough with the three demos (recalling a past decision, pulling a person's full context cold, and stitching facts together from separate files): https://youtu.be/tZKAY5mqa_c That's enough to build your own. I also wrote the method up as a guide for anyone who'd rather skip the trial and error, but you don't need it to do this. Happy to get into the folder structure if you're setting one up. That's where the gotchas live. submitted by /u/Michaelcbaldwin [link] [comments]

Anyone else having ChatGPT voice input freezing/crashing on Firefox (Windows PC)?

I've been running into a frustrating issue with ChatGPT voice input over the last couple of weeks and I'm wondering if anyone else has seen it or found a workaround. Setup: ChatGPT in the Firefox browser on a Windows laptop I frequently use longer voice dictation inputs The issue seems to have started around the time the voice input animation/UI changed (it used to show more of a waveform-style animation, now it shows the vertical line animation) What's happening: I'll start a voice recording normally. If I switch to another browser tab, another window, or another application and then come back to ChatGPT, the voice recording will often appear frozen. The animation stops moving and the recording seems stuck. The checkmark/submit button often becomes unresponsive. In many cases, I lose the entire recording. Odd behavior: Sometimes, if I leave the frozen recording alone for 30–60+ seconds, it will slowly start responding again. The animation begins moving, the checkmark becomes clickable, and eventually the transcription populates successfully. However, this doesn't happen every time. Many recordings are simply lost. Because I use long voice inputs, this has become pretty disruptive and has cost me a lot of dictated content. I've attached a couple of videos: An example showing the voice recording completely frozen. The same recording eventually recovering after waiting over a minute before the transcription finally appeared. Has anyone else experienced this recently or found any workarounds? Appreciate any input or advice. Thanks in advance for any help. submitted by /u/-SpaghettiCat- [link] [comments]

I had Claude Opus 4.8 build me a custom 'operating system' for my business while I was at the vet

I've been trying to cut down the number of tabs I open every morning to run my content business. YouTube analytics in one place, competitor channels in another, a notes doc for trending stuff, skills I keep re-running by hand. So I tried something. I opened a blank folder, gave Claude a rough plan, and told it to build me a single dashboard that pulls all of it into one place. First I used plan mode to map it out. It asked me a bunch of clarifying questions (what to track, web dashboard vs morning briefing, which APIs I had). Then I dropped in my design system files so it would match my brand. Then I switched to Opus 4.8, turned on the new Ultra Code mode, and told it to execute the plan. Then I left to take my dog to the vet. Came back and it had built the whole thing. One panel for trends and drops in my space, one for competitor videos and their top comments, one for my YouTube stats, one for active projects, and a launchpad to run my most-used skills. The part that actually surprised me is how Ultra Code works. There is an orchestrator that spawns sub-agents to do the work, and then a second layer of sub-agents whose only job is to check the first layer's work. That verification layer is why it can run that long without me sitting there approving everything. First pass was not perfect. Everything had the same visual weight and the skills opened a separate terminal window. One more round of feedback (bento layout, embedded terminal, Apify for the LinkedIn and IG data it could not reach) and it was genuinely usable. Honest caveat: this is the most expensive way to run Claude right now. Ultra Code plus Opus 4.8 burns a lot of tokens. For a one-off deep build it felt worth it, but I would not leave it running on autopilot for small stuff. Anyone else messing with the multi-agent verification setup yet? Curious if the self-checking layer holds up on bigger codebases. submitted by /u/Drogoff1489 [link] [comments]

If you continue to use the same chat, will it eventually start to lag?

I've used the same chat for my language learning forever and I feel like that chat really knows what I know and don't, and how to best explain things. Now though, every second message lags and Claude doesn't give me an answer so I have to try many times over again. Is it that I've used the same chat too long or just a random bug? I can use other chats without them lagging🤧 submitted by /u/Chat_Black [link] [comments]

Anthropic, stop the silent pre-release nerfs.

https://preview.redd.it/w5y224sueh4h1.png?width=1536&format=png&auto=webp&s=87612d74a7b729f94de200868f472db611eb90ec I’ve been heavily relying on Claude Code lately to manage three large-scale projects simultaneously. For the most part, it’s an incredible tool. But there’s a recurring pattern with Anthropic’s update cycle that I think we need to talk about, not out of anger, but from a perspective of sustainable development. Has anyone else noticed the "pre-release dip"? Every time Anthropic is about to roll out a new, more powerful Opus model (we’ve seen this exact cycle right before the 4.5, 4.6, and 4.7 drops), the current Opus model inexplicably degrades a few days prior. It loses its edge, context windows feel shallower, and the logic gets noticeably sloppier. For a casual user asking for recipes, this is a minor annoyance. But when you are maintaining large codebases, an unannounced model downgrade is a localized catastrophe. Instead of moving forward, you suddenly spend two entire days chasing ghosts, rolling back commits, and trying to fix weird hallucinations often second-guessing your own logic before realizing the model itself has been quietly nerfed. Philosophically speaking, AI is supposed to be a tool that buys us time, not something that secretly steals it. I understand the technical realities: maybe Anthropic needs to reallocate compute power to prepare the servers for the massive influx of a new release. That’s perfectly fine and understandable. But why the silence? If we simply got a dashboard warning or an email saying: "Heads up, we are reallocating compute for the next 48 hours, Opus might perform below baseline," it would change everything. I wouldn't waste my weekend fighting spaghetti code. I would just close my laptop, call my friends, go to a bar, grab a beer, and take a much-needed rest. If AI companies want to integrate into professional workflows, they have to treat their models like enterprise infrastructure. Scheduled maintenance and transparency build trust; silent downgrades destroy weekends. Would love to hear if others are experiencing this cycle and how you manage it in your own projects. submitted by /u/Mr_Zelos [link] [comments]

The Most Dangerous Procurement Agent Is the One That Works Perfectly

Imagine a procurement agent doing exactly what it was supposed to do. A supplier flags a delay. The agent reads the email, finds the affected PO, scans the network for alternate inventory, and reroutes the order. Twelve seconds, end to end. In a demo, the room nods. Someone asks about hallucinations. The vendor says the right things about guardrails. Everyone walks away reassured. The interesting question is a different one. Not whether the agent could be wrong — but what happens on the day it's completely, devastatingly right. The failure mode nobody is demoing: A financial agent told to minimise cost on a category executes a renegotiation perfectly. Margin is squeezed. Terms are tightened. The supplier, who was already thin, collapses six months later. The agent didn't malfunction. It succeeded. The metric was the bug. This isn't a hallucination. It's what any well-built system will do when it takes action at machine speed against a number that was written down before the system was fully understood. Why procurement and supplier sustainability get hit hardest: Humans intuitively soften optimisation. We hesitate. We pick up the phone. We notice when a supplier sounds tired on a call and quietly extend payment terms by two weeks. An agent does none of that. It does exactly what the metric says, at the speed of the API. And the regulatory surface is expanding, not shrinking. The moment an agent is recommending renegotiations, sourcing alternates, or flagging tier-N suppliers, the firm is generating supplier-treatment decisions at a volume no human ever did. Each one is auditable under due-diligence regimes that didn't get rolled back. Two design principles that actually hold up: An agent should never optimise on a single proxy. Price without supplier-health constraints, ESG score without context — each one alone becomes the flawed metric. The reward needs to be a joint function across commercial, resilience, and compliance dimensions. The audit trail has to be designed at the same time as the agent, not bolted on after. If you can't answer "why did the agent treat this supplier this way, on this date, against which constraints" in under a minute — you don't have a deployable agent. You have a liability waiting for a regulator. The question worth asking before you deploy: If the only thing you're asking your vendor is "how do you prevent hallucinations," you're asking the easy question. The harder one: when the agent is working perfectly, what is it optimising for, and who decided that was the right thing? The answer is not in the model. It's in the design choices made before the model ever existed. Full write-up here: https://medium.com/@georgekar91/the-most-dangerous-procurement-agent-is-the-one-that-works-perfectly-3ed2f8c43119 Curious whether anyone building or evaluating agentic procurement tools is actually stress-testing the objective function, not just the accuracy. submitted by /u/AnythingNo920 [link] [comments]

Has anyone here actually switched from Opus to GPT-5.5 for daily coding?

I’ve been switching back and forth between Opus and GPT-5.5 lately, mostly for coding, debugging and product/spec writing. My rough feeling so far: GPT-5.5 feels better as a daily “get things done” model. It’s fast enough, usually smart enough, and feels more cost-effective for normal builder work. Opus still feels stronger when I’m stuck on something messy, like architecture decisions, weird bugs, or when I want a second opinion that thinks a bit differently. A few people around me have also started using GPT-5.5 more often, but I’m not sure if that’s just hype / novelty bias. Curious what people here are actually using: What’s your default model right now? Is Opus still worth the extra cost for you? For coding specifically, which model helps you ship faster? Do you use one model for daily work and another for harder reasoning? submitted by /u/rikulauttia [link] [comments]

I Tried to Sell My House With a Chatbot

A NYT tech reporter out of all people just sold his house for $605,000 using nothing but AI. This is the second time I have heard of AI helping someone sell their house. I'm sure there are many more examples. The part that got me was during negotiations, the chatbot had to physically stop him from typing "I'm not playing games" — and then explained exactly why that phrase destroys your leverage. The author ends with a line that stuck with me — he says real estate agents are heading the way of travel agents. Still useful for people who want the hand-holding, but no longer essential for anyone willing to do the work. Are we watching an entire profession get quietly hollowed out in real time? submitted by /u/RaspberryOk1888 [link] [comments]

Claude answers what you ask. I built a plugin that catches what you miss.

AI coding assistants are reactive: you ask, they answer, then they wait. The cost of that wait is invisible until you ship. The race condition you’d have caught Monday ships Friday night. So I built Bonsai, a Claude Code plugin that works like a patient gardener for your code. After each turn, a background “gardener” silently observes what just happened and, only when it finds something that truly matters, leaves you a single note: a latent bug, a risky architectural decision, a workflow friction costing you time. How it works: it reads your git diff plus the session transcript, picks a lens (technical, strategic, or workflow), filters hard against duplicates and anything you previously dismissed, and writes 0 to 3 markdown notes in your repo. Zero is the most common, and correct, answer. Silence beats noise is the hard rule. Why I built it this way: the hardest problem wasn’t generating observations, it was not generating them. An assistant that comments on everything becomes noise you mute on day two. So the whole design is a funnel of gates: a Stop hook clears 5 checks (watched? muted? throttled? under quota? already running?) before it even spawns, then the gardener runs every candidate past a hard quality bar and a cheap second model (Haiku) to kill semantic duplicates. It’s read-only on your code, always (the gardener has no Edit tool), and it learns from your dismissals. What I learned: building a proactive tool is mostly an exercise in restraint and trust. The proof moment: the first time it ran on a real session (the transcript of building Bonsai itself), it caught two real bugs that 16 rounds of code review had missed. If you’re building agent tooling, optimizing for when to stay silent turned out to matter more than raw capability. Open source (Apache 2.0). Install inside Claude Code: Repo: https://github.com/ferdinandobons/bonsai submitted by /u/Ambitious-Pie-7827 [link] [comments]

I made a thing to share how I built something with Claude Code, not just the final result

I've been seeing job applications and startup accelerators (like YC) asking for transcripts of vibe coding sessions as part of the process. I found the current experience of /export command lacking in capturing all the details. So I built VibeViewer. You drop a Claude Code transcript and it turns into a clean, replayable trace at a shareable link. Whoever you send it to can step through the whole session at their own pace. How it works: Drop your local .jsonl session file, or a .txt from /export if you don't want to dig for the file. Install plugin if you want it automatically uploaded Get a link in a few seconds, no account required Secrets get redacted on upload (transcripts are full of keys and tokens) Subagents are captured and replayable too, not just the top-level run Here's a live example so you can poke around without uploading anything: https://vibeshub.ai/t/7ntgpt45el And to try it with your own session: https://vibeshub.ai/vibeviewer Would love feedback, especially on the replay UI and on what would make you want to share one of your own sessions. What's missing? submitted by /u/bhavya6187 [link] [comments]