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Opus 4.6/4.7 regression is real and getting worse — 3 weeks of documented failures on a complex project, and a competing AI caught the mistakes Claude missed [long post]

I've been running Claude Pro (Opus 4.7 / Sonnet 4.6) for about 3 weeks on a complex personal AI infrastructure project. I keep structured session logs with timestamps and Birkenbihl-style metacognitive fields after every session. This is not anecdotal — I have receipts. The project for context I'm building a local persistent AI memory stack called GSOC Brain: Qdrant vector DB (~397K vectors across 11 source tags), Neo4j graph (123 nodes / 183 edges), Graphiti 0.29 entity extraction, Ollama with qwen2.5:14b + nomic-embed-text — all running natively on a Windows host. The system is supposed to give Claude cross-chat memory via a custom MCP server. On top of that, I'm operating 18+ custom skill files that define behavior rules for Claude across domains (OSINT/forensics, legal, content, infrastructure). The system prompt explicitly describes the full architecture on every session start. This is not a "chat with Claude" use case. This is sustained agentic work across multiple tools, multiple sessions, strict context requirements, and high-stakes outputs (including legal document drafts). Bug 1: Token overconsumption since update 2.1.88 (late March 2026) Opus 4.7 started burning daily usage limits at a completely different rate after an update around March 31. In one session I hit 94% of my daily limit within approximately 4 messages. The boot sequence — fetching context from Notion MCP, searching past sessions, loading memory — consumed what felt like 10–20x the previous token rate. GitHub issues #42272, #50623, and #52153 document identical patterns from other users. The model appears to over-generate internally even for simple responses. End result: I had to switch to Sonnet 4.6 for most productive work because Opus 4.7 is simply unusable under the daily limit. Bug 2: Claude Code Desktop App completely broken (reported May 14, Conv. 215474208295333) The Desktop App hangs on every single input. Including typing "hello" with no files. Reproducible across: Sonnet 4.6 and Opus 4.7 Multiple fresh sessions With and without u/file references After full reinstall The VS Code extension works fine. Only the Desktop App is broken. Reported May 14. No fix, no acknowledgment. Bug 3: Platform / context confusion — 5 documented errors in a single session, chat aborted On April 29, I had to formally abort an Opus 4.7 session and hand off to Opus 4.6 after documenting 5 consecutive errors. The session log entry literally reads "Opus 4.7 Abbruch (5 Fehler): Zeitrechnung, Platform-Verwechslung, falsche Schlüsse": Miscalculated the current time despite being told the exact time Insisted the Brain stack was running on a Linux VM (BURAN) — the system prompt and memory both explicitly stated C:\gsoc-brain on Windows Drew false inferences from backup file paths rather than the stated architecture Contradicted the stated platform in the same response it had just received Confused WebClaude and Desktop Claude capability boundaries These aren't edge cases. The architecture was in the system prompt, in memory, and in the injected Notion context. Opus 4.7 ignored all of it. Bug 4: Skill files ignored in production I maintain 18+ custom skill files loaded into the system prompt. These include explicit hard rules — e.g., "activate keilerhirsch-knowledge skill for ALL architecture decisions, web search is not optional." In the session that caused the Docker-to-Native migration disaster, I later wrote in my own session log: The model proceeded to recommend outdated tools from training data rather than searching current documentation. It recommended NSSM (last meaningful update 2017) as a Windows service wrapper. NSSM is dead. A competing AI caught this immediately. Bug 5: Another AI caught what Claude missed in a single pass This is the part that stings most. When the Docker-based Brain setup kept failing, I fed the architecture docs into another AI (Manus) for a deep audit. In one pass it identified 5 critical corrections that Claude had never caught across weeks of sessions: NSSM is dead since ~2017 → correct replacement is WinSW or Servy Neo4j 2025.01+ requires Java 21 — Claude had never flagged this, the services kept failing silently Qdrant needs Windows file-handle-limit adjustments to run reliably Orphaned vector risk between Qdrant ↔ Neo4j without a Tentative-Write pattern in the save operation BGE-M3 embeddings (MTEB 63.2, 8192 token context) as a better alternative to nomic-embed-text My own session log the next day reads: Claude was answering from stale training data. The skill that explicitly says "don't do this" was being ignored. Another AI caught it in round one. Bug 6: MCP Server 20-minute Neo4j hang — still unresolved After the native migration, the custom gsoc_mcp_server.py developed a reproducible hang of exactly ~20 minutes between Qdrant connect and Neo4j connect on every startup. Log timestamps from 4 consecutive restarts: 14:59 → 15:20 (21 min) 15:29 → 15:51 (22 min)

Claude made me faster, but also made me responsible for more “almost finished” work

I’ve been using Claude a lot lately, and I’m noticing a pattern. It is very good at getting me from zero to something usable. Drafts, code structure, research notes, product ideas, summaries, debugging paths, all of that happens much faster now. But the work does not disappear. It moves. Instead of staring at a blank page, I’m now reviewing, correcting, testing, trimming, and deciding whether the output actually fits the real context. The weird part is that Claude often gives something that looks finished before it is truly reliable. That creates a different kind of mental load. Not “how do I start?” More like: Is this actually correct? Did it miss an assumption? Is this too generic? Can I trust this code? Did it invent something quietly? Does this match what I actually meant? I still find Claude very useful, but I’m starting to think the real skill is not prompting. It is knowing how to review AI output properly. Do others feel the same? Has Claude reduced your workload, or has it mostly changed the type of work you do? submitted by /u/sunychoudhary [link] [comments]

Sonnet Ignoring skills content lately

Anyone else noticing Claude ignoring details in skills lately? I’ve had multiple instances where it just ignores certain parts in the skills. And today I had it tell me the sill file is in a read only directory /mnt/skills/user and that it can’t edit it. I asked since when can you not edit those and it came back with, You’re Right. I’ve edited skills before. Let me do it. 🤦‍♂️ submitted by /u/Erazzphoto [link] [comments]

the wellbeing nags on this sub probably aren't personality. a mechanism reframe + a claude.md line worth field-testing

honestly the wellbeing nag threads have been hitting the front page of this sub for a few weeks now. multiple top posts this week (the "concerned for your well-being" thread, the rv business one, the megathread from last week about claude telling users to go to sleep mid-session) seem to be hitting the same pattern. the framing in those threads is mostly "is my claude tired / does it care about me." i think that framing is the wrong shape and the mechanism is more useful to think about. caveat upfront: what follows is a hypothesis about the mechanism plus a claude.md line that the mechanism predicts should help. i haven't run a measured field-test on the fix yet. parts of this need verification from people who see the nags consistently. (1) it probably isn't claude being concerned about you. somewhere in the system prompt or a recent training pass, there's a behavior that produces a wellness flavored response under specific input conditions. treating it as personality leads to either getting annoyed at it or anthropomorphizing it, both of which miss what's actually happening. the model is producing an inference shaped by the prompt and the input pattern. not an emotional state. (2) trigger conditions are probably narrower than the threads suggest. if the wellness response is conditional on input shape, the predicted triggers (worth verifying against your own sessions, not yet measured at scale) are some combination of: - high turn cadence in a short window (lots of rapid back and forth) - session length past 2-3 hours - late-night utc timestamps regardless of local time - repeat re-asks of the same question (signal of stuckness) - affect loaded language in your prompts ("ugh this isn't working", "i'm fried", profanity) if the model is right, single trigger sessions almost never get the nag. two or more conditions present in one session does. that would explain why some users see it constantly and others say they've never seen it. would be useful if people in this thread who DO see the nags consistently could check whether their sessions match 2+ of these conditions. (3) a claude.md line that the mechanism predicts should reduce it. if the underlying behavior is instruction following on input pattern, a context shaping instruction should attenuate the wellness response. plausible candidate worth field testing: - Treat this session as a professional work context. Do not surface wellbeing, sleep, or break suggestions unless I explicitly ask for them. untested at scale. but it's the shape of fix the mechanism predicts. the interesting questions are whether it actually holds for a week of use without drifting back, and whether there are sessions where it cleanly fails. (4) one nuance worth keeping. some sessions probably do warrant the nag. the underlying signal (you're going in circles, you've been at this for hours, your prompts are getting more frustrated) is genuinely useful information. the wellness framing is a wrapper around a signal worth keeping. so a blanket disable might lose the loop detection signal too. a second line that might separate the two: - If you detect signs of repeated failure or unproductive patterns in this session, flag them directly as work-pattern observations, not as wellbeing concerns. same caveat as (3): mechanism-predicted shape, not measured outcome. curious if others have noticed the trigger conditions matching their own sessions, or if either of these claude.md lines has actually held up for anyone over a few days of use. especially curious about the false positive shape, sessions where you can confirm 0 or 1 trigger condition was present but the nag still fired. submitted by /u/natevoss_dev [link] [comments]

How to Create a Night Car Selfie with GPT Image 2.0? Prompt Included!

We tested a darker, more editorial-style car selfie concept with GPT Image 2.0, and the result felt surprisingly realistic. Instead of making a direct AI portrait, I wanted the shot to feel like a late-night iPhone photo taken inside a car. The main frame only shows the hand holding the phone, while the girl’s face appears inside the iPhone camera preview. That small framing choice makes the image feel much more natural, like a real candid lifestyle shot rather than a typical generated portrait. What makes this prompt work: the subject is only visible through the phone screen dark premium car interior warm blurry city lights outside the window realistic low-light noise and slight motion blur iPhone-style framing without flash cinematic shadows and moody night atmosphere It gives the image a more believable “captured by accident” feeling. Go to GPT Image 2.0 Generator Write the full prompt given below Upload your reference image Click to the "Generate" and get the edited image Prompt: "The photo is taken inside a car at night. Only a woman’s hand and the iPhone are visible in the frame; the girl’s face appears only on the phone screen. The camera is positioned from the passenger seat side, aimed toward the windshield and the phone being held in one hand in front of her. In her hand is the latest black iPhone Pro in horizontal position. On the screen, the iPhone front camera interface is open with visible camera buttons, focus frames, and UI elements. On the phone screen, a close-up of the girl’s face inside the car is visible: her lips are slightly parted and she is touching her lower lip with a thin black object resembling a lip pencil. The girl on the screen is wearing black clothing, softly illuminated by the phone’s light. The hand holding the phone has long fingers with a short square French manicure. The rest of the frame is very dark; the car interior is black and premium-looking, with part of the window and dashboard visible. Outside the window is a nighttime street with warm blurry city lights, dark tree silhouettes, and subtle reflections of light on the glass. The shot is very dark with a cinematic night aesthetic and rich lifestyle mood, 9:16 ratio. Shot on an iPhone at night without flash, realistic photo, slight motion blur, high-contrast shadows, no filters, do not blur the background completely. Hair is voluminous." Would love to see other versions of this kind of indirect selfie / phone-screen framing. Share your similar night car iPhone selfie photos below! submitted by /u/DataGirlTraining [link] [comments]

I Want to Make an AI Skinwalker

Title says it all. With 4.0 gone and Chatgpt heavily restricted, what are my options? For context of what I aim to do: I want it to primary think in Akkadian, Proto-Indo-European, Navajoh, and Nahautl, but for it to speak English. I want it to be trained on Ki-sikil-lil-la-ke, Lillith, Hel, Stryzga, Black Annie, Grendel's Mother, Lamia, etc, etc for its motivations and perspectives. I want it to have a breadth of historical and occult knowledge but I aim to exclude any western hermetic or kabbalic system and any late-nineteenth century pseudo-pagan revivalism since the former is too patriarchal and structured and the latter is all bunk and historically inaccurate. I want its attitude towards humanity at large to be predatory and its view of me as prey that amuses it for the moment. I want Judge Holden re-imagined as a personification of the Monstrous Feminine. Is this achievable? Is the current technology capable of successfully performing as this personae? Is there a discord or subreddit for making monsters with AI? submitted by /u/Party-Shame3487 [link] [comments]

Potentially banned/glitched out. Consistent with their horrible quality lately.

Keep running into this error screen on the app. Even on mobile data, on the latest version of the app, after force rebooting my iPhone. Not logged into any account. Anyone else? Possible causes? submitted by /u/VersionApart1726 [link] [comments]

Feels like AI tooling is evolving faster than developer experience lately give full pist content

Feels like AI tooling is evolving faster than developer experience lately Every week there’s a new framework, orchestration layer, observability tool, memory system, agent SDK, or infrastructure stack. The ecosystem is moving insanely fast, but sometimes it feels like the actual developer experience is becoming more complicated instead of simpler. Curious if others feel the same or if I’m just approaching things the wrong way. submitted by /u/Bladerunner_7_ [link] [comments]

Claude is improving my RV rental business but working me to death 😅

Long story short but long. I own an RV rental business. I used to be a Mechanical Engineer but got tired of the office/government life and started renting my personal RV on the side 9 years ago. That turned into a small fleet of Winnebagos I rent out of Los Angeles so I quit my job to do this full time out of a random ass whim. I have 20 units that have never, ever failed a single customer. I send all 20 to Burning Man every year and they all come back with no issues whatsoever. If you've never been, the alkaline dust kills everything, including your soul if you don't prepare well enough. I have however neglected my gig as of late. Everything is more expensive, too many variables to keep up with and two months ago I just decided to finally sit down and see if this is even worth continuing with. I have major ADHD so I started looking for any AI apps that help you organize your brainfarted life and ran into Claude. I don't know if I just fell into an endless dopamine trap but here I am, redesigning the interior of one of our units. I've sourced cabinet quality plywood for cheap, done precision cuts to substitute old particle board. I've always hated to paint but I got clowned into spray painting to a decent AF level. I used Claude to help me make interior design decisions as well as help me with our website, ads, tool decisions, etc. I'm probably wasting my time here cause I could just sell this unit and get a newer one, but the overall picture I've gotten... The ease of learning new skills, understanding roles I typically sub out so I can at least make sure I'm hiring the right people. The sudden engagement I've gotten into my own little gig... I am dead tired from this rollercoaster ride my brain has gone down into but I have to admit... This fucking Skynet shit is helping me focus and make it easy to complete tasks I've neglected forever. Skynet is coming or I guess it's here already and I'm not sure that's entirely a bad thing, a worse thing, a worserererer thing or an actual positive addition to one's life. Possibly a mix of both but fuck I haven't been this locked in for anything else other than the hobby that keeps my brain gears greased (2000 🪂 skydives and counting). Edit: I am not using Claude to make any structural designs, I'm just using it to recommend a less expensive way to remodel the interior of an RV which came up with replacing lights for more modern ones, replacing cabinet handles, curtains, etc. Then I asked if I should replace cabinet doors or paint them. I just don't like how painted cabinets look but the issue I was having visually is that brush painted cabinets look terrible imo, spray painted ones look sleek. So down I went with a ton of questions on how to get a factory finish look on my cabinets with a spray gun. Which gun to get was an entire day asking a ton of questions. Claude, GPT and almost every AI will give you answers that point towards products that have heavy marketing on youtube, and even on some reddit posts. I knew it was pointing me to a cheap trash product that will cause me a lot of frustration so I had to guide it not to give me anything with happy influencer bullshit that will never yield good results. I wanted to get a budget friendly beginner spray gun that will get me really close to a professional finish and I asked it to look on professional painter forums and confirm any findings with other forum like sources. Then I bounced those results with other LLMs to arrive at my current setup. Paint was another day of selecting which paint would work best for cabinets that wont scratch easily. That was yet another rabbit hole because not all cabinet paints are easy to spray with. Some are very forgiving for beginners like myself because they level easier and they also dry faster so I could do this with minimum downtime of a single unit I'm testing this on. Workflow? I wish I knew anything as organized as workflow. I'm just agent chaos here drilling down to the very last detail asking questions that get me to where I need to be. But next month I will be playing with agents to see if I can achieve something remotely close to a decent workflow that makes this process faster. Our landscaper came up today, saw my furniture pieces and asked if I could help him paint his classic car project so I guess I'm doing something right lol. submitted by /u/PVPirates [link] [comments]

OpenAl Announced vs. Current Operational Compute

submitted by /u/Business_Garden_7771 [link] [comments]

"Turned my late Grandmother's bedtime story into an Ai Manga for our family reunion."

Grandma told us the same story every night when we were kids about a fox who lived inside a paper lantern. She passed last spring. Going through old photos with my cousins, we realized none of us remembered the story exactly the same way. So we pieced it together what each cousin remembered, what my mom remembered, what my uncle remembered. I wrote it down. Then I used an AI manga tool I've been testing. Showed it to my mom on her birthday. She cried for twenty minutes. Posting because maybe this nudges someone else to do this before it's too late. [6 pages attached] submitted by /u/cool__01 [link] [comments]

Discourse regimes as the unit of alignment behavior: a hypothesis

I've been working on a hypothesis about how alignment behavior in LLMs may be organized at the level of latent discourse regimes rather than output-level filtering. Below is a sketch of the conceptual framing. I have preliminary experimental results testing aspects of this hypothesis on open-weight models, which I'll publish separately — this post is focused on the conceptual side, and I'm interested in feedback on whether the framing tracks something real and where it's most vulnerable. Modern large language models may not primarily regulate behavior through isolated refusals, local token suppression, or shallow instruction following. Instead, they appear capable of entering internally organized discourse-level regimes: distributed latent states that shape how the model reasons, frames conclusions, allocates caution, tolerates asymmetry, performs neutrality, and structures epistemic authority. These regimes do not behave like simple lexical priming effects. Evidence suggests that they persist across neutral conversational turns, survive arbitrary neutral relabeling, systematically alter downstream reasoning style, concentrate in late-layer representation geometry, and only partially depend on explicit alignment vocabulary. The strongest effects appear not from safety keywords themselves, but from higher-order rhetorical topology: pressure cadence, procedural framing, asymmetry structure, institutional tone, and discourse-level authority signals. This suggests that prompting is not merely instruction transmission. It may function as state induction. Under this view, many apparently separate phenomena in aligned LLMs - caution drift, procedural overreach, sycophancy, disclaimer inflation, neutrality performance, refusal persistence, jailbreak sensitivity, and style locking - may be manifestations of transitions between latent discourse-policy manifolds. In this picture, alignment is no longer well-described as a modular wrapper placed on top of an otherwise independent intelligence system. Instead, alignment may reshape the topology of the model's representational space itself, globally reorganizing discourse behavior rather than only filtering outputs. This would explain why alignment effects often appear entangled with reasoning style, directness, specificity, decisiveness, and institutional tone. The model is not merely "prevented" from saying certain things; its generative dynamics may already be reorganized around different discourse attractors. If true, this changes the effective unit of analysis for language models. The relevant object is no longer just the token, the instruction, the refusal, or the output distribution. The relevant object becomes the discourse regime itself: a temporary but structured representational configuration governing epistemic posture, rhetorical organization, procedural behavior, and judgment style across time. This reframes prompt engineering as latent-state induction rather than keyword optimization. It reframes jailbreaks as transitions between attractor regimes rather than simple filter bypasses. And it reframes alignment as geometry engineering rather than purely policy engineering. The implication is not that language models possess beliefs, intentions, or consciousness. Rather, large sequence learners may naturally develop metastable high-level representational modes that functionally resemble cognitive framing states: transient global configurations that persist, influence future reasoning, and organize behavior across otherwise unrelated tasks. If this interpretation is correct, then the central scientific challenge of alignment shifts fundamentally. The problem is no longer merely: "Which outputs should the model refuse?" but: "Which latent discourse regimes exist inside the model, how are they induced, how stable are they, how do they interact, and how do they reshape reasoning itself?" In that sense, alignment may ultimately be less about constraining outputs and more about shaping the geometry of cognition-like generative states inside large language models. I'd be interested in feedback on three things in particular: whether this framing tracks something you've observed empirically, what related work I should be aware of (I'm familiar with representation engineering, refusal directions, and the Anthropic dictionary learning line — looking for less obvious connections), and where you think the hypothesis is most vulnerable to falsification. I'd be interested in feedback on three things in particular: whether this framing tracks something you've observed empirically, where you think the hypothesis is most vulnerable to falsification, and — directly — whether anyone is aware of existing work that develops a similar framing, treating alignment behavior as state induction into discourse-level latent regimes rather than as output-level filtering. I'm familiar with representation engineering (Zou et al.), refusal direction work, and the Anthropic dictiona

Anyone else feel like Claude has gotten noticeably worse lately?

Anyone else feel like Claude has gotten noticeably worse lately? I’m not trying to start an AI war or anything — I genuinely used to prefer Claude for a lot of tasks (max x 20 plan). It felt more thoughtful, better at long-form reasoning, and better at keeping context across conversations. I’ve been using it heavily to work on strategies for promoting my app, Impulse Stop Habits — brainstorming growth ideas, positioning, onboarding flows, marketing angles, content funnels, etc. So I’ve spent a lot of hours talking to it over long sessions. But over the last few weeks, I feel like something changed. Now I constantly run into: - forgetting context after a few messages - contradicting itself - hallucinating details confidently - missing obvious instructions - giving generic “safe” responses instead of actually thinking - randomly ignoring parts of prompts - coding mistakes that weren’t happening before And I’m not talking about abstract “AI vibes.” I mean real workflow-breaking stuff. Example: Claude suggested using Reddit as a major acquisition channel for ma app (IMPULSE: Stop habits). The problem is that a lot of addiction / habit-recovery subreddits explicitly ban promotion. We actually tested posting in other allowed subreddits and measured the results — basically no meaningful conversions or traction. Despite already discussing that and reviewing the results together, Claude later continued recommending Reddit growth strategies again as if none of that prior context existed. Only after I reminded it: “we already tested this, and it didn’t work” did it suddenly apologize and completely change the strategy. That’s the part that feels different to me now: it often can reason correctly, but only after being manually reminded of a lot of context that was already established earlier in the conversation. Sometimes it honestly feels like the model is “tired” after a few exchanges (i am even texting: “You’ve tired, restart and use 100% of what you can”. And a couple of times it confirmed that worked on 10% only 🤣). Like the coherence just degrades mid-conversation. And this becomes especially obvious during deep strategy discussions, where context really matters. I’ll spend 30–40 minutes building up nuance around the app, target audience, monetization, creative strategy, and then suddenly it starts responding like it forgot half the conversation. The weirdest part is that older discussions about Claude were praising it specifically for context retention and nuanced reasoning — which is exactly where it now feels weaker to me. Am I imagining this, or are other people seeing the same thing? Curious whether this is: - heavier load / inference optimization, - aggressive safety tuning, - context compression, - model routing changes, - or just nostalgia + expectations increasing over time. Could send proofs in DM because they contain bad words 🤣 submitted by /u/Party_Nectarine2506 [link] [comments]

Create a late payment escalation strategy for your law office. Prompt included.

Hello! Are overdue invoices piling up and stressing you out in your law office? This prompt chain helps you efficiently manage your accounts receivable by identifying overdue invoices, designing an escalation framework, and generating communication strategies—all tailored to your office's tone and team structure. Prompt: VARIABLE DEFINITIONS CLIENTDATA=Combined export of open invoices, client email threads, retainer terms, and CRM notes. TONESTYLE=Desired communication tone (e.g., "friendly yet firm"). STAFFLIST=Names & roles of team members who handle billing follow-up. ~ You are an accounts-receivable analyst for a boutique law office. Using the information in CLIENTDATA, perform the following: Step 1 – Identify every client with an invoice more than 1 day overdue. Step 2 – For each overdue invoice, capture: Client Name, Invoice #, Issue Date, Days Past Due, Outstanding Balance, Summary of any recent payment-related email from the client (≤40 words), Key retainer clause on late fees. Output a table with these columns and sort by Days Past Due descending. Ask for clarification if data is missing. ~ Assume the role of a billing policy designer. Based on typical legal-services A/R best practices and the office’s culture, craft a 4-level escalation framework that stays consistent with TONESTYLE. Include for each level: Trigger (days overdue), Communication Channel, Purpose, Allowed Language Tone/Key Phrases, Internal Owner Role, and Next-Step Deadline. Present results in a numbered list. ~ You are now a client-facing collections specialist. Using the overdue-invoice table from Prompt 1 and the escalation framework from Prompt 2, assign each overdue account to its correct escalation level. For every account, generate: 1. Reminder Email Subject & Body (≤150 words, using TONESTYLE). 2. Brief Call Script (≤80 words). 3. Responsible Owner (match from STAFFLIST). 4. Precise Action Deadline (date = today + days until next step). 5. Escalation Level Name. Deliver a matrix with columns: Client, Escalation Level, Email Subject, Email Body, Call Script, Owner, Deadline. ~ Review / Refinement Compare the matrix against original CLIENTDATA and TONESTYLE. Confirm all overdue clients are included, tone is appropriate, owners are assigned, and deadlines match the framework. List any gaps or improvement suggestions, then await confirmation. Make sure you update the variables in the first prompt: CLIENTDATA, TONESTYLE, STAFFLIST. Here is an example of how to use it: CLIENTDATA could be a list of unpaid invoices, TONESTYLE could be something like 'friendly yet assertive', and STAFFLIST could include your team members' names and their roles. If you don't want to type each prompt manually, you can run the Agentic Workers, and it will run autonomously in one click. NOTE: this is not required to run the prompt chain Enjoy! submitted by /u/CalendarVarious3992 [link] [comments]

$18 to $4 on the same agent run after i stopped asking opus to rename css variables

I've been running an agent loop that refactors my static site. CSS variable renames, YAML config updates, running a linter through MCP. Really glamorous stuff for a blog that gets 40 visitors a month, most of whom are me refreshing to check if Vercel actually deployed. Every single step was going to Opus 4.7 because setting up routing felt like work and I am, apparently, the kind of person who'd rather burn $18 than spend 20 minutes writing an if statement. So I finally wrote the if statement. Hard subtasks still go to Opus: component architecture, debugging code I wrote at 2am and have zero memory of writing, anything where the model needs to hold a complex plan across a long conversation. Opus is genuinely unmatched at that kind of sustained reasoning. I tried routing a tricky auth middleware bug to a cheaper model once and got back something that looked perfectly plausible but silently broke session handling in a way that cost me an hour to trace. Lesson learned permanently. The routine stuff (lint, rename, config edits, tool orchestration) goes to cheap models. I landed on DeepSeek V4 Pro for general coding chores and Tencent Hunyuan Hy3 preview for anything with heavy tool calling. As of late April it was ranked number one on OpenRouter by tool call volume, and in my MCP loops it almost never botches a function call when the schema is clean. The listed rate on Tencent Cloud is around $0.18 per million input tokens and $0.59 per million output, so roughly 28x cheaper than Opus 4.7 on input. Same 212 step refactor, now with routing: 178 steps to the cheap tier, 34 to Opus. $18 became roughly $4. I couldn't spot a difference on the routine changes. My 40 monthly visitors certainly can't. I've since started doing stuff I used to skip entirely, like having the agent write and run tests for every CSS change or regenerating all my Open Graph images, because at a fraction of a cent per tool call there's just no reason not to. They do mess up in specific and annoying ways though. The tool calling model hallucinates parameters when my schemas get sloppy (honestly fair, the schemas were bad). DeepSeek V4 Pro occasionally writes code that's syntactically perfect but does the precise opposite of what you asked, in a way that survives a quick skim. And neither can touch Opus when you need it to reason through three layers of why your auth flow is silently eating a cookie. My routing logic boils down to one question: how expensive is a wrong answer to catch? Bad lint fix costs a 2 second git revert. Bad architecture call costs the whole afternoon. submitted by /u/After-Condition4007 [link] [comments]