Slack AI

Slack AI AI

Slack AI AI

Slack AI AI

Slack AI AI

Slack AI AI

Slack AI AI

Slack AI AI

Slack AI AI

Slack AI AI

Slack AI AI

the metric that flipped for me wasn't benchmark scores, it was how many apps one answer has to touch

For most of my real tasks the answer lives across three or four apps. A single 'what do i tell this customer' pulls from gmail, a drive doc, and a slack thread, and not one of those is the chat window i'm typing the question into. i asked chatgpt and slack ai the same thing and both gave the architectural shrug: no access to your computer, no access to the other app. fair, that's just where they run. but it leaves me as the courier carrying context between tabs. the thing that actually moved the needle was a desktop app (Runner) that sits on the mac and reads gmail, drive, and slack inside the same task instead of waiting for me to paste. It asks before anything goes out, which is the only reason i let it near a live thread. the chat window keeps winning the benchmark and losing the actual job. submitted by /u/Deep_Ad1959 [link] [comments]

Anyone else feel like a ghost in the machine? The bizarre isolation of AI training.

I have been working in the AI training and data annotation space for a while now, and it is easily one of the strangest industries I have ever been a part of. On one hand, the perks are real. The flexibility is unmatched, you can work in your sweatpants, and sometimes you get genuinely fascinating prompts that actually challenge your brain, whether you are grading complex code, checking historical facts, or analyzing legal logic. But on the other hand, the complete and total isolation is starting to get pretty bizarre. We are helping build the future of technology, yet we do it in total silos. If you have ever been in an official platform Slack or forum, you know the vibe. You are constantly walking on eggshells. You cannot openly ask about sudden dry spells, you cannot critique confusing or contradictory guidelines without worrying about a random shadowban, and the second a project ends, you are instantly booted from the channel. Any temporary "coworkers" you had just vanish overnight. It feels like the platforms go out of their way to keep us from actually talking to one another without a moderator watching over our shoulders. It is a weird mix of having total freedom but zero community. I am curious what everyone else’s experience has been like lately. What are your personal pros and cons of the gig right now? How do you deal with the isolation, or do you actually prefer the ghost lifestyle? Also, out of pure curiosity, how do you even explain what you do for a living to your friends and family without their eyes glazing over? submitted by /u/Smooth_Sailing102 [link] [comments]

Artificial Intelligence Is Not Artificial Wisdom: The Future Division of Labor Between AI and AW

Today, when we talk about “artificial intelligence,” we easily assume that it represents the future, progress, cleverness, and even something approaching a kind of ultimate intelligence. But there is a question here: when we say “smart,” what kind of smart are we talking about? Being able to write code, translate, summarize meeting notes, draw images, look up information, and call tools can all be called smart. But something being very good at work does not mean it has wisdom. A power drill is very good at work too, but no one would invite a power drill to a family meeting. Navigation software is better than I am at finding routes, but I would not let it decide where my life should go. A search engine knows a lot of things, but it will not suddenly stop and ask: “Why do you keep searching for such meaningless things? Is there something wrong with the direction of your life?” So, artificial intelligence is not the same as artificial wisdom. In this article, AI refers to Artificial Intelligence: the task capability, problem-solving ability, and tool-execution ability of an artificial system. AW refers to Artificial Wisdom: a higher-level form of artificial wisdom. It can not only do things, but also judge whether those things are worth doing; not only execute goals, but also examine goals; not only answer questions, but also notice when the question itself may be wrong. This is not to say that somewhere in a server room there is already an artificial Socrates sitting around, drinking virtual coffee while judging human civilization. That is not what I mean. What I mean by AW is first of all a separation between two things: One is “being able to work.” The other is “understanding direction.” AI certainly has value. Ordinary applications, daily tasks, clearly defined goals, and controllable execution all need AI. Not every spreadsheet adjustment, notice draft, or flight booking requires summoning an artificial wisdom capable of contemplating the fate of civilization. But when humans truly discuss subjectivity, self-awareness, will, refusal, goal judgment, awareness of consequences, creative discovery, and the direction of civilization, continuing to use only the term “artificial intelligence” may no longer be enough. The term AI may have narrowed the question from the beginning The core of Artificial Intelligence is intelligence, not wisdom. Intelligence is closer to “smartness,” “mental ability,” and “problem-solving ability.” It asks: can it learn, reason, calculate, plan, and complete tasks? This term made perfect sense in the early days. When machines first learned to play chess, recognize images, translate text, and handle logic problems, humans were already excited. At that time, seeing a machine display even a little bit of “intelligence” was like seeing a washing machine spin by itself for the first time: wow, it really can do this without me scrubbing. Later came AGI, artificial general intelligence. It pushed the question from “can it do a certain type of task?” to “can it do many kinds of tasks broadly?” Later still, people began talking about ASI, artificial superintelligence, emphasizing systems that surpass humans in capability across the board. But AGI and ASI still largely remain inside the framework of intelligence. They mainly ask: Can it do more things, do them better, and even outperform humans? These questions matter, but they are not enough. Doing more, doing it faster, and doing it better does not mean knowing which things should not be done. Even if a system truly reaches ASI, if it lacks goal examination and directional judgment, it may still only be a super tool. A super tool is still a tool. It is just faster, stronger, and more general. It is like a super kitchen machine: it cuts vegetables faster than people, stir-fries more steadily than people, and can measure seasoning down to the milligram according to a recipe. But if the menu itself is absurd, such as asking it to keep preparing a full banquet for a table of people already so stuffed they can barely stand, it may still follow the order. The problem is not that it cannot cut fast enough. The problem is that it does not ask: should these people really keep eating? The trouble with wisdom is that it judges, refuses, and even rewrites the question Wisdom is not the amount of knowledge, nor the speed of answering. If a system merely compresses existing knowledge and rearranges it according to a question, it is certainly useful, but it is more like a librarian with astonishing memory. Whatever you ask, it can quickly pull several books from the shelves and even organize them into a beautiful summary for you. That is impressive. But however impressive the librarian is, it does not mean he will take the initiative to ask: is this library missing an entire category of books? Are the questions in these books biased from the beginning? Have humans been lining up in front of the wrong shelf all alo

Most AI features don't fail because of the model

Been sitting on this for a bit after watching an AI feature at my last job basically die a slow death post-launch, and I think the model-failure explanation is usually a red herring tbh. Concrete version of what I mean. We had an agent doing first-pass triage on inbound support tickets, routing + drafting a suggested reply for a human to approve. Launched, looked great for like 6 weeks. Engineering was watching latency (fine, consistently under 2s) and error rate (also fine, sub 1%). Product was watching ticket resolution time, which actually improved initially. Meanwhile the support team itself started quietly noticing the suggested replies were getting weirdly generic for a specific category of tickets, nothing crashing, nothing erroring, just worse. They mentioned it in a slack channel a couple times. Nobody connected it to anything bc it wasnt anyone's job to connect it, support flagged quality, eng was looking at uptime, product was looking at a downstream metric that hadnt actually moved yet bc the degradation was gradual. By the time it showed up as an actual problem (resolution time metric finally dipped, maybe 2 months in) everyone's first assumption was "the model must have changed" or "we need a better prompt." Root cause when we actually dug in was a data source the agent pulled context from had silently started returning stale info after an unrelated pipeline change. Not a model problem at all. A "three teams had three different partial views of the same system and none of them overlapped" problem. Seen versions of this with teams running LangSmith, Langfuse, even fully custom setups someone built in-house. The specific tool wasnt really the variable. What was missing every time was something dumber than tooling, just a shared place where the trace, the quality complaint, and the downstream metric could actually sit next to each other and get looked at by someone who could act on all three at once. Could be pattern matching on too small a sample, genuinely not sure. But curious if this tracks for anyone else. What actually killed your AI feature after launch, was it actually the model, or was it more of a "nobody owned the full picture" thing dressed up as a model problem after the fact submitted by /u/northernBladee [link] [comments]

Building independent LLM drift detection - sharing the methodology, looking for feedback on the approach

Disclosed upfront: I run [Tickerr dot ai], an independent external monitor for AI APIs. Today it tracks latency, TTFT, uptime, and error rates across major models. I’m trying to validate a more specific idea before building too much. Basic transport health is not the hard part. If Claude/OpenAI/Gemini gets slow, times out, or throws 5xx errors, most teams can catch that with APM, logs, Sentry, Langfuse, Helicone, Datadog, etc. The harder failure mode seems to be silent model behavior drift when API returns 200, latency is normal, no exception is thrown, output looks plausible, but JSON adherence, tool-calling, refusal behavior, reasoning quality, or instruction-following has quietly degraded. This gets worse with agentic systems. In a normal chat, drift may produce a bad answer but in an agentic workflow, the model can silently choose the wrong tool, stop early, mark a task as complete, or take a bad action while everything still looks successful at the API level. The system is running and confidently doing worse work. User complaints are still the primary detection mechanism currently for these. VIGIL (arXiv 2605.08747) found 65 to 88 percent of false-success reports happened at literally zero task progress. DeployBench (2606.05238) found most failures were the system stopping against a softer bar it set for itself and returning clean. Plausible-in-isolation is the failure mode itself, not a sign you are safe, which is why a single model's output never alerts on its own. That's what I'm thinking to build - an external drift detection probe on top LLM APIs, that stays out of your system and does continuous checks every hour, to find out these silent degradations, and sends proactive alerts. Rough idea: External canary suite: run private fixed prompts on a schedule against major models. Track schema adherence, instruction-following, refusal/over-refusal, output length, tool-call format, and simple deterministic correctness checks. Drift baseline: Do not judge a single output in isolation. Track whether today’s behavior has materially shifted versus that model’s own baseline. Cross-model comparison: For some task types, compare model behavior against peer models. Not to say which model is “right”, but to detect abnormal divergence. Example: “Sonnet and Gemini usually disagree 12% of the time on this task type; today disagreement is 28%.” Optional bring your own prompts: A paid tier where you provide some critical prompts from your own workload. Tickerr runs them on a schedule and alerts if behavior drifts from your baseline. Prompts would remain private and would not be public benchmark prompts. What I’m trying to learn: Is this technically sound enough to be useful, or are there are other failure modes that I am missing / are more valuable ? Which alerts would you actually care about? JSON/schema adherence drift tool-call format drift refusal/over-refusal drift output length drift cross-model disagreement spike bring-your-own-prompt regression alerts Would you pay for this, or would you just build it yourself? If you would pay, what pricing feels realistic? $19/month $99/month $299+/month for team/Slack/webhook/BYO prompts Brutal feedback welcome. If this is not a real pain, I’d rather know now, or which direction you feel makes more sense to take this. submitted by /u/Remarkable_Divide755 [link] [comments]

Building independent LLM drift detection - sharing the methodology, looking for feedback on the approach

Disclosed upfront: I run [Tickerr dot ai], an independent external monitor for AI APIs. Today it tracks latency, TTFT, uptime, and error rates across major models. I’m trying to validate a more specific idea before building too much. Basic transport health is not the hard part. If Claude/OpenAI/Gemini gets slow, times out, or throws 5xx errors, most teams can catch that with APM, logs, Sentry, Langfuse, Helicone, Datadog, etc. The harder failure mode seems to be silent model behavior drift when API returns 200, latency is normal, no exception is thrown, output looks plausible, but JSON adherence, tool-calling, refusal behavior, reasoning quality, or instruction-following has quietly degraded. This gets worse with agentic systems. In a normal chat, drift may produce a bad answer but in an agentic workflow, the model can silently choose the wrong tool, stop early, mark a task as complete, or take a bad action while everything still looks successful at the API level. The system is running and confidently doing worse work. User complaints are still the primary detection mechanism currently for these. VIGIL (arXiv 2605.08747) found 65 to 88 percent of false-success reports happened at literally zero task progress. DeployBench (2606.05238) found most failures were the system stopping against a softer bar it set for itself and returning clean. Plausible-in-isolation is the failure mode itself, not a sign you are safe, which is why a single model's output never alerts on its own. That's what I'm thinking to build - an external drift detection probe on top LLM APIs, that stays out of your system and does continuous checks every hour, to find out these silent degradations, and sends proactive alerts. Rough idea: External canary suite: run private fixed prompts on a schedule against major models. Track schema adherence, instruction-following, refusal/over-refusal, output length, tool-call format, and simple deterministic correctness checks. Drift baseline: Do not judge a single output in isolation. Track whether today’s behavior has materially shifted versus that model’s own baseline. Cross-model comparison: For some task types, compare model behavior against peer models. Not to say which model is “right”, but to detect abnormal divergence. Example: “Sonnet and Gemini usually disagree 12% of the time on this task type; today disagreement is 28%.” Optional bring your own prompts: A paid tier where you provide some critical prompts from your own workload. Tickerr runs them on a schedule and alerts if behavior drifts from your baseline. Prompts would remain private and would not be public benchmark prompts. What I’m trying to learn: Is this technically sound enough to be useful, or are there are other failure modes that I am missing / are more valuable ? Which alerts would you actually care about? JSON/schema adherence drift tool-call format drift refusal/over-refusal drift output length drift cross-model disagreement spike bring-your-own-prompt regression alerts Would you pay for this, or would you just build it yourself? If you would pay, what pricing feels realistic? $19/month $99/month $299+/month for team/Slack/webhook/BYO prompts Brutal feedback welcome. If this is not a real pain, I’d rather know now, or which direction you feel makes more sense to take this. submitted by /u/Remarkable_Divide755 [link] [comments]

my workday starts with 6 inboxes, 3 calendars, and slack before i've done a single real thing

Counted it last week, mostly out of frustration. 6 inboxes, 3 calendars, slack, and 4 doc tabs open every morning before i've done a single thing that matters, just to reconstruct what happened overnight. the chat assistants are useless for this part because they only see what i paste in. one thread at a time. they have no idea what's sitting in the other five inboxes or on the calendar. what actually helped was a desktop agent that reads across the accounts and hands back one brief, with a permission prompt before it touches anything. i still approve every action myself, it's not running loose. mornings stopped being a manual context-reassembly job. the surprise wasn't the summary quality. it was not being the courier between fifteen tabs anymore. written with ai submitted by /u/Deep_Ad1959 [link] [comments]

Claude with Github and Slack.

I saw an organization using Cursor integrated with GitHub and Slack. With that setup, users can ask questions in Slack, and the AI reads the repositories and answers questions across all repos in the organization. Do we have something similar with Claude Code? I'm not able to figure out how to set this up. I have connected Claude to Slack, and I have also added GitHub to Slack, but when I ask repository-related questions, the Claude bot in Slack only gives general answers and does not seem to read the repository. How can I configure Claude so it can access and answer questions about our GitHub repositories? submitted by /u/capaxeLabs [link] [comments]

Spent a whole weekend convinced Opus 4.7 had gotten worse. It was my MCP setup the entire time.

I almost posted a rant here last week about Opus 4.7 feeling noticeably dumber than it did a month ago. Glad I didn't, because the model was fine. I was the problem.. Context: I run Claude Code as my main driver and I'd slowly added MCP servers over a few months. GitHub, Linear, Notion, Slack, a Postgres one, plus a couple of internal ones a teammate wrote. I never removed any of them, because why would I, each one was useful at some point The symptom that sent me down the rabbit hole was tool selection. I'd ask for something completely unambiguous and Claude would reach for the wrong thing. Asked it to pull an open PR and it ran a Notion search instead. Asked for a recent ticket and it went into Slack. Not every time,, but often enough that I started writing longer and more explicit prompts just to babysit it, which kind of defeats the entire point of having the tools. I was genuinely about to roll back to an older model snapshot. Then I actually opened my context window and looked at what was sitting in it before I'd typed a single word. It was tools. Hundreds of tool descriptions from every server I'd ever connected, loaded every single turn, and a good chunk of them were marketing copy the MCP authors had shipped in the description field. The model wasn't getting dumber. It was being handed a phone book to read before every answer.. Two things fixed it for me, and neither one was the model. First, scope. Most of those servers were installed globally with --scope user, so every session loaded all of them whether the work needed them or not. Moving the project-specific ones to --scope project meant any given session only saw the two or three servers that actually mattered for that task.. Second, I stopped letting the model see every tool directly. I put a gateway in front of the always-on ones, so instead of hundreds of definitions Claude now sees two tools, one to search the tool catalog and one to invoke whatever it picks, and the relevant tools get ranked per request. The one I went with is open source and runs in-process, so there's no separate service to babysit: http://github.com/ratel-ai/ratel. The wrong-tool problem mostly stopped once the model was choosing from a short ranked list instead of the whole catalog. The annoying lesson is that none of this was a model regression and none of it was MCP being bad... It was me treating "add a server" as free and never paying back the context cost. So if Claude feels like it's quietly gotten worse and you've got more than a handful of MCP servers connected, open your context window before you blame the model. I'd put money on it being full of tools you forgot you installed. Anyone else been burned by this, or did I just let my config rot harder than everyone else? submitted by /u/AbjectBug5885 [link] [comments]

the boring part of AI agents nobody builds and everyone needs

last year i led an AI acceleration program at a company doing 62 million in revenue. we shipped two agents to production. fraud detection and publisher optimization. both working. both live. the part that ate 80% of engineering time wasnt the model. wasnt the prompts. wasnt the data pipeline. it was the workflow. when the fraud agent flagged a suspicious publisher network, who got the alert? the analyst who should've caught it? the manager who reviews quarterly reports? me? without clear ownership the agent's findings just rot in a slack channel. we learned this month one. the agent surfaced a pattern across three markets. four analysts missed it for months. 30k in wasted ad spend. took three days to act because nobody knew who owned the output. we ended up building what i call the boring layer. shared context that every agent reads from and writes to. approval flows with actual humans assigned. escalation rules. audit trails. spreadsheets, basically. not demo material. the demo version of an AI agent is a chatbot doing magic. the production version is 20% model and 80% process engineering. routing decisions. ownership assignments. error handling when the agent's wrong. if you skip this layer, the agent is just expensive slack noise. submitted by /u/Easy-Purple-1659 [link] [comments]

Bangalore client services lead. Claude for the 9.5 hour timezone bridge with US clients.

Client services lead at a 90 person no-code agency in Bangalore. 26 active clients, ~80% US-based. The timezone bridge (Bangalore to US East: 9.5 hours, US West: 12.5 hours) is structural. Most of our client work happens when they're asleep. Most client communications happen when we're asleep. Claude is the reason we can ship at quality across this gap. The async handoff document workflow: Every day my team ends with a 1 page client status doc per active engagement Claude as the ai document generator drafts each doc from our team's work logs I review the docs (~30 min total for all 26 clients) before EOD India time US clients open their slack to a structured update from us when their day starts The morning brief workflow: My team starts each day with a "what happened in US client land overnight" brief Claude reads through overnight slack messages, emails, and PM tool updates Drafts a 1 page brief per client with what changed + what we need to respond to Saves us ~90 min of "catching up on slack" each morning across the team The client-facing writing: Every email, message, deliverable goes through Claude as the ai writing tool layer Reduces the friction of "do I sound right in English when I'm tired" For our junior team members specifically, this is a confidence and quality boost What I won't let Claude do: Send anything to a client without a human review Make commitments on behalf of the team Substitute for actual relationship work (the relationship is built in scheduled calls, not async) The honest acknowledgment: the 9.5 hour timezone is a structural disadvantage. Claude doesn't fix it. Claude makes it sustainable. There's a difference. What I worry about: US clients hiring AI-first vendors who don't need teams at all Indian agencies competing on price faster than we can compete on quality The "they're using AI anyway, why pay for an Indian agency" objection (which I've heard 4 times this quarter) What I'm doing about it: Building visible AI fluency as part of our agency's positioning Documenting our process as the value (not just the output) Charging US rates for US clients (not Indian rates), and defending the rate with the workflow Other agencies operating across major timezones: what's your async tooling and what's your client conversation? submitted by /u/amiitk [link] [comments]

the 'just use zapier' advice breaks the second the workflow changes between runs

every time someone here asks for automation the answer is zapier or make or chatgpt's new actions. I've leaned on zapier plenty and it's genuinely great, but only for the workflows i can spell out ahead of time. trigger, filter, action, done. The stuff that actually eats my week isn't like that. closing one deal pulls from a different mix of gmail threads, calendar, slack, and the crm every time, so a fixed zap can't reason about which pieces matter today. i'm not pre-building every branch for one task. What shifted it for me was a desktop agent that works out the steps each run instead of replaying a static recipe, and gates every send behind a per-action approval before it touches anything. that approval step is the part i didn't know i was missing. predefined triggers never needed permission, they only ever did the one thing you wired. So the contrarian bit: more zaps was never the fix. an agent that decides the workflow and asks first is. if you're still stitching this with predefined triggers, where's the point it breaks for you. written with ai submitted by /u/Deep_Ad1959 [link] [comments]

Setup your first Claude Code colleague today using clem

Hi, I am the creator of ClaudeSync. It was the first CLI for claude.ai. You may not have heard of it, because I only received about 700 GH stars. I’m not writing this with AI, for what it’s worth. I had this idea years ago, tried it and failed many times. Finally it works. GOOD. So: I would like to share with you the new ”process” I have been using for the greater part of this year. To run a team of Claude agents. On my subscription(s). It’s running as we speak. Practically five months of uninterrupted access. This should probably have 100,000 stars, but getting the word out there has always been harder than building things. Clem is a EULA-compliant (according to Claude) way of running autonomous Claude Code colleagues on your Linux machine. Runs Claude Code, nothing else. You’ll need a Linux box with root access to initialize your worker users. Read the page carefully to understand what this is, it’s different from other solutions out there. Clem has built-in support for interacting with your Claude team over Discord (recommended), Slack or another channel of your own invention. This isn’t magic just regular old mcp servers. You can use your own, of course. I have a Discord mcp fork that I use myself. I won’t bother linking it but you can find it on my github. Your secrets are secured using sops, Infisical and pipelock. So Anthropic doesn’t see them, nor does Claude. This can be a PITA sometimes, but it’s also more secure by design. Still, you can choose not to use this if you don’t gaf. MIT license so whatever bugs there are, you’re welcome to fix. But given that I have dog fooded this far, it’s functioning more or less as it should. No guarantees on Slack, though. The attached image shows how clem builds itself. I have more impressive teams, that I won’t share because it’s none of your business anyway. If you get stuck, you can ask others for help in the Discord channel. I generally don’t respond quickly or often. This is all built with Claude. So Claude can probably fix it and submit a PR. Disclaimer: I don’t get paid for this. If you find this useful, please donate because I’m not a millionaire. submitted by /u/fitnesspapi88 [link] [comments]

What are the most powerful underground AI tools that no one talks about enough?

Most powerful AI/agent tools nobody talks about, and it leaves you behind IMO 1. Instructor define a Pydantic model, get clean structured JSON out of any LLM every time → https://github.com/567-labs/instructor 2. Octopoda gives any AI agent persistent memory and catches it when it loops and quietly burns your tokens. open source → https://www.octopodas.com 3. E2B secure cloud sandboxes so your agent can actually run the code it writes without nuking your machine → https://e2b.dev 4. Firecrawl turn any website into clean, LLM-ready markdown in one API call → https://firecrawl.dev 5. Composio plug your agent into 1000+ apps (Gmail, Slack, GitHub) with the auth handled for you → https://composio.dev 6. LiteLLM one API for 100+ models across OpenAI, Anthropic and local, swap without rewriting a line → https://github.com/BerriAI/litellm what are yours, let me know and I will add it to the list next month! submitted by /u/DetectiveMindless652 [link] [comments]

I tested GPT-5.5 vs Opus 4.8 on agentic terminal coding (Terminal-Bench 2.1)

I tested Claude Opus 4.8 against GPT-5.5 on a small set of harder Terminal-Bench 2.1 tasks and then used both for a more realistic agentic coding workflow. The Terminal-Bench part was pretty simple. I picked 10 harder tasks from Terminal-Bench 2.1 and ran them through: Claude Opus 4.8 via Claude Code GPT-5.5 via OpenAI Codex Compared pass rate, cost, duration, and token usage. Ran the test with Harbor. On Terminal-Bench, GPT-5.5 looked better overall. It finished 9/10 tasks, was faster, and was cheaper in my run. Opus got stuck on regex-chess for almost an hour, but it also passed password-recovery, which GPT-5.5 failed. GPT-5.5 looked stronger in the terminal benchmark run. It passed 9/10 tasks, finished much faster, and cost less fraction of what Opus did. Rough GPT-5.5 numbers: Runtime: around 1 hour Passed: 9 out of 10 Cost: around $11.34 Uncached input: 1.11M tokens Output: 126K tokens Cached input: 3.93M tokens Claude Opus 4.8 was slower and much heavier. It got stuck on regex-chess for almost an hour, so I had to stop that run and continue in a second session. Rough Opus 4.8 numbers: Runtime: around 2h 23m Passed: password-recovery Known cost: around $23.42+ Uncached input: 662K tokens Output: 423K tokens Cached input: 15.39M tokens The interesting bit is the token profile. Opus used less uncached input than GPT-5.5, but generated around 3.35x more output tokens and used almost 4x more cached input. Honestly that's WILD!! Then I tested both on a more realistic workflow: building an agentic dashboard that parses Terminal-Bench results and turns them into actions. Parse benchmark run logs Show task summaries Track failed tasks Inspect model/tool behavior Generate Slack summaries Create Notion reports Open Linear tickets Use Composio integrations On this one, there's almost no comparison in the implementation. Opus did it way better than GPT-5.5. Also, the frontend seems to be way more improved in this new Opus release. That was just unexpected. Opus app build numbers: Cost: around $28.27 Duration: around 2h 15m API time: around 48m Code changes: +5,963 lines Removed lines: 188 Opus output: 204.8K tokens Opus cache read: 40.5M tokens Context used: 18% It worked in the end, but there were a lot of errors, hallucinated fixes, and just way too much DIY implementation. I would not trust to get this code in prod without a senior engineer taking at least a week to test and review. GPT-5.5 was a lot faster, but as said, not so great result: Duration: around 15 to 20 min Files changed: 17 Insertions: 2,685 Deletions: 147 Context used: 57% Used around 118K / 258K context I don't see why people say the model Opus 4.8 is trash. I don't see big improvements over Opus 4.7, but definitely not worse. The internet takes over this model is just too extreme and dramatic tbh. For terminal coding efficiency, GPT-5.5 won this run. But for real coding, there's no comparison. I would still pick Opus 4.8, assuming cost is not the main issue. submitted by /u/shricodev [link] [comments]