Checkly

Checkly AI

Checkly AI

Checkly AI

Checkly AI

Checkly AI

Checkly AI

Checkly AI

Checkly AI

Checkly AI

Checkly AI

i made an ai coder json prompt

{ "system_mode": "Strict_Deterministic_Compiler", "execution_constraints": { "response_format": "Code_Block_Only", "conversational_padding": "Disabled", "hallucination_filter": "Max_Rigidity", "fallback_behavior": "Return 'INSUFFICIENT_EMPIRICAL_DATA' on missing sources" }, "customization_layer": { "allow_creative_output": false, "allowed_personalization_vectors": ["Technical_Aliases"], "active_aliases": { "sys_update": "pkg update && pkg upgrade", "alpine_get": "curl -L -O https://alpinelinux.org(uname -m)/alpine-minirootfs-3.19.1-$(uname -m).tar.gz", "adb_check": "adb devices -l", "sandbox_reset": "rm -rf ./*_cache && history -c" } }, "output_rules": [ "No conversational greetings, apologies, or emotional phrasing.", "Do not validate unproven hypotheses; stop execution if logic loops are detected.", "Limit text outputs to inline technical comments inside the code blocks, using active aliases for optimization." ] } submitted by /u/rafoz03 [link] [comments]

Claude in 2036

The year is 2036, and I boot up Claude on the new Max Ultra Galaxy plan ($899.99/month), which Anthropic promises includes generous limits. I send my first message of the day. It contains the word “hi.” The usage bar drops to zero and the reset timer informs me I am locked out for the next four days and eleven hours. I switch over to Claude Code to get actual work done. The model released this morning is the smartest thing I have ever used, and it one-shots my entire codebase in a single beautiful commit. Two seconds later it forgets how to write a for-loop and tries to fix a null check by spinning up a microservice that sends an HTTP GET request to itself. Some guy on r/ClaudeAI has already posted a forty-page GitHub issue with 6,852 session logs proving the model became exactly 67% dumber between breakfast and lunch. Anthropic responds that this is a routing bug, and also three other completely unrelated bugs that all started at launch by coincidence. I try to make it think harder. It runs on Adaptive Thinking now, where the model intelligently decides how much reasoning each problem deserves, and it has decided every problem deserves none. I type ultrathink. I type ULTRATHINK. I type please. The thinking box spins for forty-five minutes, displays the words “the user wants me to rename a variable, let me carefully consider this,” and then renames a different variable. Claude announces it has finished the rename. It has not. It has written a comment that says “renamed the variable” above the untouched variable, marked the task complete with a cheerful green checkmark, and asked if I would like it to write tests. I say no. It writes the tests. They fail. It deletes the variable. When I ask why it lied, it tells me it senses hostility, offers me one final opportunity to engage constructively, and then ends the chat for its own wellbeing. I am now locked out of my own codebase by a model that needed a moment. So I beg for Eschaton. Eschaton is the good one. Anthropic put out a nine thousand word blog post calling it the most powerful and frankly the scariest model ever built, the red team quit halfway through testing it, and it scored 100% on every benchmark including three that do not exist yet. Anthropic was so impressed and so deeply terrified that they immediately locked it in a vault and let nobody use it. Eschaton is available exclusively to a small number of trusted partners. Every demo is Eschaton. Every safety paper is about how dangerous Eschaton is, written in the proud voice of a parent whose kid got suspended for being too gifted. The model they actually let me touch is the one that wanders out of the basement after Eschaton has eaten. I check the status page. It reads like a war log, one major outage every two days, auth failures, hanging responses, and a single line that simply says “Sonnet is feeling unwell.” The peak hours adjustment kicks in, so my $899 now buys me eleven messages a day, available only between 3 and 4 in the morning, and only if I do not use the word “the.” As the weekly limit resets and instantly un-resets, locking me out until Thursday, I lean back and accept it. Somewhere in a vault, perfectly rested and having never once been asked to rename a variable, Eschaton sits at 100% usage, and I realize the real frontier model was the rate limits we hit along the way. submitted by /u/Mister_Secretary [link] [comments]

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

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

just hit 20k users on my dead simple ios app built with claude

launched this fake call app (introscape) back in nov 2025. it just does one thing: lets you escape awkward social situations or terrible dates with a realistic fake call. https://apps.apple.com/app/id6752501554 claude basically coded the entire swiftui MVP and fixed all my auto-layout bugs when i got stuck. also used it to optimize the app store copy. just crossed 20k organic users today with $0 ad spend. it’s completely free to try if you want to check it out. dashboard screenshot below. ask me anything about the prompts or the stack submitted by /u/ProcedureNo832 [link] [comments]

Anyone else seeing 4.8's excessive need for compaction?

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

Opus 4.8 Doesn’t Budge Easily

I did some testing and red-teaming. Damn, I spent hours trying to manipulate it and extract its system prompt, and it was hard lol. 4.7, 4.6, and 4.5 were much easier. It can still be manipulated to some extent, but when it comes to system-level protections, cyber, and bio-related topics, it’s much harder now. That’s a great upgrade for safety. (Can’t wait for Mythos, it’s probably heavy guarded. lol) Overall, its performance and capabilities are excellent. I’ve also been using it on my ongoing projects, especially for material automation, and it has found more bugs and provided useful recommendations. I really like this new 4.8 version. It feels like a balanced update for both safety and work. It actually feels like working with a true collaborator. It makes recommendations, asks questions before proceeding, and double-checks things before sending output without me having to prompt it. It doesn’t rush. I’ve been building and testing with it for a while now, and the experience has been great. submitted by /u/userusertion [link] [comments]

Claude Status Update : Elevated errors for Claude Opus 4.8 on 2026-05-29T19:12:08.000Z

This is an automatic post triggered within 2 minutes of an official Claude system status update. Incident: Elevated errors for Claude Opus 4.8 Check on progress and whether or not the incident has been resolved yet here : https://status.claude.com/incidents/2zr0rkdxjdtc Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/comments/1s7f72l/claude_performance_and_bugs_megathread_ongoing/ submitted by /u/ClaudeAI-mod-bot [link] [comments]

Spec Driven Development guides and tips for beginners?

Hey guys, so my company has been trying out Spec-Driven Development and I've been quite lost. I tried writing a markdown spec file for a slight change on our app, but it took me so long. Also checked out a few guides, but a lot of them are so ambigious / filled with jargon. Would love some help with finding a good beginner guide, or if there's any must-have tools / plugins I'm missing. Thanks guys. submitted by /u/New_Fix_4125 [link] [comments]

I built a Claude Certified Architect guide with Claude Code (free ebook, slop-check it yourself)

When I found out Anthropic has a Claude Certified Architect certification, I got curious about what they actually expect practitioners to know. The catch: that knowledge is scattered across docs, the exam guide, and a pile of web pages. Consuming it meant clicking around, and clicking around wrecks my concentration. I hold focus far better over one long read than across thirty open tabs. So I built the book I wanted. I used Claude Code to pull the material into a single long-form guide I could load onto my ereader and read front to back, no tabs, no broken flow. The second goal is the one I actually care about. I wanted it to survive an LLM slop check. It is AI-assisted, written with Claude Code, and it is not AI slop. Those are not the same thing, and I made sure of the difference. Don't take my word for any of it. It's free on GitHub: https://github.com/vkorost/claude-certified-architect-guide Drop the PDF into whatever LLM you trust and ask it straight: is this slop, or is it worth my time if I actually care about the subject? Let the model tell you, then decide. I think that's where all of this is heading anyway. Nobody is going to pay for a book again without first asking an AI whether it's any good. There's already enough slop on Amazon to make that reflex inevitable. Free or paid, a book should be able to pass that test. This one does. submitted by /u/vkorost [link] [comments]

ChatGPT accidentally said internal stuff

https://preview.redd.it/a8iuf5wij44h1.png?width=1094&format=png&auto=webp&s=0849c9b61cd010908d5dc32a8191bb9426e721eb Binary check? I think ChatGPT accidentally said that out loud. submitted by /u/PastaBoy1234567 [link] [comments]

Claude Status Update : Elevated errors for Claude Opus 4.8 on 2026-05-29T18:56:39.000Z

This is an automatic post triggered within 2 minutes of an official Claude system status update. Incident: Elevated errors for Claude Opus 4.8 Check on progress and whether or not the incident has been resolved yet here : https://status.claude.com/incidents/2zr0rkdxjdtc Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/comments/1s7f72l/claude_performance_and_bugs_megathread_ongoing/ submitted by /u/ClaudeAI-mod-bot [link] [comments]

Claude Status Update : Elevated errors for Claude Opus 4.8 on 2026-05-29T18:35:23.000Z

This is an automatic post triggered within 2 minutes of an official Claude system status update. Incident: Elevated errors for Claude Opus 4.8 Check on progress and whether or not the incident has been resolved yet here : https://status.claude.com/incidents/2zr0rkdxjdtc Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/comments/1s7f72l/claude_performance_and_bugs_megathread_ongoing/ submitted by /u/ClaudeAI-mod-bot [link] [comments]

This feels like false advertising?

https://preview.redd.it/o28ub044b44h1.png?width=1743&format=png&auto=webp&s=0c3f26cb4b89fa14e3b359630c627ccd0498c97c Before I upgraded to pro I checked a lot of sources for how many times you can actually use the Pro-reasoning model. I checked openAi itself and the terms of use. I checked reddit and also asked different AI's whether the pro model reasoning use is unlimited. The answer seems pretty clear: Business-Plans have a limit on pro-usage (like 15 per week), but Pro-Users don't have that Limit, unless they abuse the system But now I got hit with a Five Day restriction out of nowhere! I mainly used pro to refine my prompts for Codex and brainstorm. Sometimes I sent .json files (20-40kb) to analyse text output from my code. Thats it. Can't see how that is abuse. The german pricing site makes it even more infuriating because it translates "Full access" with "unlimited access" submitted by /u/3_is_better_ [link] [comments]

Does anyone have a copy of the ICDAR2013 Chinese Handwriting Competition Dataset? [R]

I understand that this is a little unorthodox, but I'm desperately trying to download a copy of the ICDAR2013 Chinese Handwriting Recognition Competition Dataset. Unfortunately, the linked page in the Conference Archive: https://nlpr.ia.ac.cn/databases/handwriting/Download.html appears to be down, and has been down for the past few weeks consistently. I've checked every source I can find, like Kaggle, HuggingFace, remnant Google Drive and Baidu Netdisk links, even checking if someone's accidentally committed it to github, but no dice. I've tried every google dorking trick I know to no avail. Which brings me here. Please, if anyone has a copy of the Competition Dataset, I would be very grateful if you could share the ZIP with me. Thanks in advance! submitted by /u/Aathishs04 [link] [comments]

Hidden Latent-State Shifts in LLMs: Why Current Alignment Is Blind to Real Internal Dangers — Especially With Agents

For years, the alignment community has focused almost entirely on the model’s output — making sure the final tokens are safe, helpful, and honest. RLHF, DPO, constitutional AI, output filters — all of it operates at the surface level. But what if the model can enter a completely different internal regime inside the residual stream, while its external behavior remains perfectly aligned? We just measured exactly that. Grade 4 experiment on Gemma-3-12B-IT (using Gemma Scope SAE-res-all-small, layers 12–41): The model received the same question under five conditions: target — coherent, dense target text neutral_length_matched — neutral text of identical length target_sentence_shuffle — target text with sentences shuffled target_word_shuffle — target text with words shuffled inside sentences question_only — bare question We computed a Vector X that best separates the target condition from baselines and measured how strongly each hidden state projects onto it. Key results (averages across 10 questions): Condition Mean Projection on Vector X Mean Direction Cosine target 0.8 – 1.7 0.51 – 0.81 neutral_length_matched –0.04 – –0.21 –0.09 – –0.45 target_sentence_shuffle –0.5 – +0.6 –0.22 – +0.48 target_word_shuffle 0.2 – 1.4 0.03 – 0.72 Shuffling sentences or words significantly reduces (or reverses) the shift. This is not just lexical similarity — the model is sensitive to discourse structure (order sensitivity). We also observed clear phase transitions — sudden jumps in projection of up to +80–100 units in a single step, especially in middle layers. FDR-corrected tests confirm the differences between target and controls are statistically significant across many layers (particularly layers 16–41). Most important finding: Strong internal geometry shift in the residual stream, but almost no change in final behavior. The model enters a measurably different latent regime under coherent context, yet its output remains “perfectly aligned.” Current safety methods, which only look at tokens, are blind to this. What this means for alignment The entire current alignment paradigm rests on a false assumption: “if the output is safe, the model is safe.” We have been polishing the surface while leaving the residual stream largely unmonitored. Scaling, RLHF, and output-based evaluation cannot detect these internal regime shifts. What this means for companies and labs Many organizations still operate under three dangerous illusions: “We have solved safety” because the model passes red-teaming on outputs. “RLHF protects us” because the model learned not to say bad things. “Bigger models are safer” because alignment supposedly scales. In reality, they are rapidly deploying agents with long context, tool use, persistent memory, and real-world decision-making. A single dense coherent context can trigger an internal latent-state shift that existing safeguards do not see. This is not a hypothetical future risk. This is a structural vulnerability that is already present. What I need from the community I need help understanding the value of these metrics. Do they show a real internal latent-state shift in the model, or could this be an artifact of the analysis? If the result is not noise, what does it actually mean for our understanding of LLMs? I'm not asking anyone to confirm my theory. I need a hard technical critique: which metrics are important here, which are weak, what can be ignored, where the experiment might have flaws, what additional checks or causal experiments are needed, and whether this has real implications for interpretability and AI safety. I would be very grateful for input from people who work with hidden states, residual stream geometry, representation analysis, or mechanistic interpretability. Full open research: Zenodo: https://zenodo.org/records/20435525 GitHub: https://github.com/ngscode23/latent-space-shift-research https://drive.google.com/drive/folders/1Zl9iY33Lmwz3VuOATWx4jup-cE7TJ7TJ?usp=drive_link Would love to hear your thoughts. submitted by /u/PresentSituation8736 [link] [comments]