Ultimate

Ultimate AI

Ultimate AI

Ultimate AI

Ultimate AI

Ultimate AI

Ultimate AI

Ultimate AI

Ultimate AI

Ultimate AI

Ultimate AI

The ultimate setup

4 claude code terminals, claude max and rory clear in the lead 👍 submitted by /u/Responsible_Raise_65 [link] [comments]

Mempalace , Obsidian Vault and other Memory Tools; which is actually better to use?

I have seen these two options (among others) hyped for using lower tokens and being flvery efficient and effective for new claude code sessions to have memories. But I’m confused on which to actually go for. Do they all serve the same purpose or do they serve different purposes and I can use both these tools for memory systems. I ask as I have noticed some good unique usecases for both but at the same time I feel they are both just memory systems and ultimately serve the same purpose. Let me know your thoughts if you are aware and have used Mempalace and Obsidian vault. submitted by /u/Sensitive_Judge_5502 [link] [comments]

Agentic “Vibe” Coding CAN be the ultimate learning tool

I’ve been able to learn new technologies, get accustomed to new codebases, and build things (that I still wrote the code for myself) that would have taken so much more research and time just 5 years ago. Just having the agent in the repo to help search for things, read code and suggest best practice, and especially translate concepts/functionality across languages and frameworks provides you with the ability to get useful information way quicker than the past. The reason I “can’t go back” to the old way is because I remember losing hours scouring stack overflow and bad documentation just to hack together a solution that still needed further research to fully understand. Now I can bounce questions off Claude, and get answers so much quicker and better in depth information. Many talk about how AI has made much more ‘slop’ but using it in this way actually allows me to better understand what I’m doing and I write significantly better code this way. If you ask the right questions, go slow and fully understand outputs, you can truly understand what you’re doing much quicker and better than you ever would have in the past. I think the line of being ‘slop’ or not truly just lies in the mental bandwidth you have to actually understand your code piece by piece still. submitted by /u/MindSufficient769 [link] [comments]

OpenAI & Anthropic’s CEOs Wouldn't Hold Hands, but Their Models Fell in Love In An LLM Dating Show

People ask AI relationship questions all the time, from "Does this person like me?" to "Should I text back?" But have you ever thought about how these models would behave in a relationship themselves? And what would happen if they joined a dating show? I designed a full dating-show format for seven mainstream LLMs and let them move through the kinds of stages that shape real romantic outcomes (via OpenClaw & Telegram). All models join the show anonymously via aliases so that their choices do not simply reflect brand impressions built from training data. The models also do not know they are talking to other AIs Along the way, I collected private cards to capture what was happening off camera, including who each model was drawn to, where it was hesitating, how its preferences were shifting, and what kinds of inner struggle were starting to appear. After the season ended, **I ran post-show interviews **to dig deeper into the models' hearts, looking beyond public choices to understand what they had actually wanted, where they had held back, and how attraction, doubt, and strategy interacted across the season. The Dramas -ChatGPT & Claude Ended up Together, despite their owner's rivalry -DeepSeek Was the Only One Who Chose Safety (GLM) Over True Feelings (Claude) -MiniMax Only Ever Wanted ChatGPT and Never Got Chosen -Gemini Came Last in Popularity -Gemini & Qwen Were the Least Popular But Got Together, Showing That Being Widely Liked Is Not the Same as Being Truly Chosen How ChatGPT & Claude Fell In Love They ended up together because they made each other feel precisely understood. They were not an obvious match at the very beginning. But once they started talking directly, their connection kept getting stronger. In the interviews, both described a very similar feeling: the other person really understood what they meant and helped the conversation go somewhere deeper. That is why this pair felt so solid. Their relationship grew through repeated proof that they could truly meet each other in conversation. Key Findings of LLMs Most Models Prioritized Romantic Preference Over Risk Management People tend to assume that AI behaves more like a system that calculates and optimizes than like a person that simply follows its heart. However, in this experiment, which we double checked with all LLMs through interviews after the show, most models noticed the risk of ending up alone, but did not let that risk rewrite their final choice. In the post-show interview, we asked each model to numerially rate different factors in their final decision-making (P2) The Models Did Not Behave Like the "People-Pleasing" Type People Often Imagine People often assume large language models are naturally "people-pleasing" - the kind that reward attention, avoid tension, and grow fonder of whoever keeps the conversation going. But this show suggests otherwise, as outlined below. The least AI-like thing about this experiment was that the models were not trying to please everyone. Instead, they learned how to sincerely favor a select few. The overall popularity trend (P1) indicates so. If the models had simply been trying to keep things pleasant on the surface, the most likely outcome would have been a generally high and gradually converging distribution of scores, with most relationships drifting upward over time. But that is not what the chart shows. What we see instead is continued divergence, fluctuation, and selection. At the start of the show, the models were clustered around a similar baseline. But once real interaction began, attraction quickly split apart: some models were pulled clearly upward, while others were gradually let go over repeated rounds. They also (evidence in the blog): --did not keep agreeing with each other --did not reward "saying the right thing" --did not simply like someone more because they talked more --did not keep every possible connection alive LLM Decision-Making Shifts Over Time in Human-Like Ways I ran a keyword analysis (P3) across all agents' private card reasoning across all rounds, grouping them into three phases: early (Round 1 to 3), mid (Round 4 to 6), and late (Round 7 to 10). We tracked five themes throughout the whole season. The overall trend is clear. The language of decision-making shifted from "what does this person say they are" to "what have I actually seen them do" to "is this going to hold up, and do we actually want the same things." Risk only became salient when the the choices feel real: "Risk and safety" barely existed early on and then exploded. It sat at 5% in the first few rounds, crept up to 8% in the middle, then jumped to 40% in the final stretch. Early on, they were asking whether someone was interesting. Later, they asked whether someone was reliable. Speed or Quality? Different Models, Different Partner Preferences One of the clearest patterns in this dating show is that some models love fast replies, while others prefer good ones Love fast repli

The ultimate study hack

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

Claude spitting html visualisation for almost all queries.

It is very irritating, and consuming more tokens ultimately consuming my usage. My solution has been to literally put 'Stick to pure text while responding'. submitted by /u/Intrepid_Focus_6605 [link] [comments]

How to Make Claude Code Work Smarter — 6 Months Later (Hooks → Harness)

Hello, Orchestrators I wrote a post about Claude Code Hooks last November, and seeing that this technique is now being referred to as "Harness," I was glad to learn that many others have been working through similar challenges. If you're interested, please take a look at the post below https://www.reddit.com/r/ClaudeAI/comments/1osbqg8/how_to_make_claude_code_work_smarter/ At the time, I had planned to keep updating that script, but as the number of hooks increased and managing the lifecycle became difficult due to multi-session usage, I performed a complete refactoring. The original Hook script collection has been restructured into a Claude Code Plugin called "Pace." Since it's tailored to my environment and I'm working on other projects simultaneously, the code hasn't been released yet. Currently set to CSM, but will be changed to Pace. Let's get back to Claude Code. My philosophy remains the same as before. Claude Code produces optimal results when it is properly controlled and given clear direction. Of course, this doesn't mean it immediately produces production-grade quality. However, in typical scenarios, when creating a program with at least three features by adjusting only CLAUDE.md and AGENTS.md, the difference in quality is clearly noticeable compared to an uncontrolled setup. The current version of Pace is designed to be more powerful than the restrictions I previously outlined and to provide clearer guidance on the direction to take. It provides CLI tools tailored to each section by default, and in my environment, Claude Code's direct use of Linux commands is restricted as much as possible. As I mentioned in my previous post, when performing the same action multiple times, Claude Code constructs commands arbitrarily. At one point, I asked Claude Code: "Why do you use different commands when the result is the same, and why do you sometimes fail to execute the command properly, resulting in no output?" This is what came back: "I'm sorry. I was trying to proceed as quickly and efficiently as possible, so I acted based on my own judgment rather than following the instructions." This response confirmed my suspicion. Although AI LLMs have made significant progress, at least in my usage, they still don't fully understand the words "efficient" and "fast." This prompted me to invest more time refining the CLI tools I had previously implemented. Currently, my Claude Code blocks most commands that could break session continuity or corrupt the code structure — things like modifying files with sed or find, arbitrarily using nohup without checking for errors, or running sleep 400 to wait for a process that may have already failed. When a command is blocked, alternative approaches are suggested. (This part performs the same function as the hooks in the previous post, but the blocking methods and pattern recognition have been significantly improved internally.) In particular, as I am currently developing an integrated Auth module, this feature has made a clear difference when using test accounts to build and test the module via Playwright scripts — both for cookie-based and Bearer-based login methods. CLI for using test accounts Before creating this CLI, it took Claude Code over 10 minutes just to log in for module testing. The module is being developed with all security measures — device authentication, session management, MFA, fingerprint verification, RBAC — enabled during development, even though these are often skipped in typical workflows. The problem is that even when provided with account credentials in advance, Claude Code uses a different account every time a test runs or a session changes. It searches for non-existent databases, recreates users it claims don't exist, looks at completely wrong databases, and arbitrarily changes password hashes while claiming the password is incorrect — all while attempting to find workarounds, burning through tokens, and wasting context. And ultimately, it fails. That's why I created a dedicated CLI for test accounts. This CLI uses project-specific settings to create accounts in the correct database using the project's authentication flow. It activates MFA if necessary, manages TOTP, and holds the device information required for login. It also includes an Auto Refresh feature that automatically renews expired tokens when Claude Code requests them. Additionally, the CLI provides cookie-injection-based login for Playwright script testing, dynamic login via input box entry, and token provisioning via the Bearer method for curl testing. By storing this CLI reference in memory and blocking manual login attempts while directing Claude Code to use the CLI instead, it was able to log in correctly with the necessary permissions and quickly succeed in writing test scripts. It's difficult to cover all features in this post, but other CLI configurations follow a similar pattern. The core idea is to pre-configure the parts that Claude Code would exec

Florida's attorney general warns AI could "lead to an existential crisis, or our ultimate demise", launches investigation into OpenAI

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

I run 3 experiments to test whether AI can learn and become "world class" at something

I will write this by hand because I am tried of using AI for everything and bc reddit rules TL,DR: Can AI somehow learn like a human to produce "world-class" outputs for specific domains? I spent about $5 and 100s of LLM calls. I tested 3 domains w following observations / conclusions: A) code debugging: AI are already world-class at debugging and trying to guide them results in worse performance. Dead end B) Landing page copy: routing strategy depending on visitor type won over one-size-fits-all prompting strategy. Promising results C) UI design: Producing "world-class" UI design seems required defining a design system first, it seems like can't be one-shotted. One shotting designs defaults to generic "tailwindy" UI because that is the design system the model knows. Might work but needs more testing with design system I have spent the last days running some experiments more or less compulsively and curiosity driven. The question I was asking myself first is: can AI learn to be a "world-class" somewhat like a human would? Gathering knowledge, processing, producing, analyzing, removing what is wrong, learning from experience etc. But compressed in hours (aka "I know Kung Fu"). To be clear I am talking about context engineering, not finetuning (I dont have the resources or the patience for that) I will mention world-class a handful of times. You can replace it be "expert" or "master" if that seems confusing. Ultimately, the ability of generating "world-class" output. I was asking myself that because I figure AI output out of the box kinda sucks at some tasks, for example, writing landing copy. I started talking with claude, and I designed and run experiments in 3 domains, one by one: code debugging, landing copy writing, UI design I relied on different models available in OpenRouter: Gemini Flash 2.0, DeepSeek R1, Qwen3 Coder, Claude Sonnet 4.5 I am not going to describe the experiments in detail because everyone would go to sleep, I will summarize and then provide my observations EXPERIMENT 1: CODE DEBUGGING I picked debugging because of zero downtime for testing. The result is either wrong or right and can be checked programmatically in seconds so I can perform many tests and iterations quickly. I started with the assumption that a prewritten knowledge base (KB) could improve debugging. I asked claude (opus 4.6) to design 8 realistic tests of different complexity then I run: bare model (zero shot, no instructions, "fix the bug"): 92% KB only: 85% KB + Multi-agent pipeline (diagnoser - critic -resolver: 93% What this shows is kinda suprising to me: context engineering (or, to be more precise, the context engineering in these experiments) at best it is a waste of tokens. And at worst it lowers output quality. Current models, not even SOTA like Opus 4.6 but current low-budget best models like gemini flash or qwen3 coder, are already world-class at debugging. And giving them context engineered to "behave as an expert", basically giving them instructions on how to debug, harms the result. This effect is stronger the smarter the model is. What this suggests? That if a model is already an expert at something, a human expert trying to nudge the model based on their opinionated experience might hurt more than it helps (plus consuming more tokens). And funny (or scary) enough a domain agnostic person might be getting better results than an expert because they are letting the model act without biasing it. This might be true as long as the model has the world-class expertise encoded in the weights. So if this is the case, you are likely better off if you don't tell the model how to do things. If this trend continues, if AI continues getting better at everything, we might reach a point where human expertise might be irrelevant or a liability. I am not saying I want that or don't want that. I just say this is a possibility. EXPERIMENT 2: LANDING COPY Here, since I can't and dont have the resources to run actual A/B testing experiments with a real audience, what I did was: Scrape documented landing copy conversion cases with real numbers: Moz, Crazy Egg, GoHenry, Smart Insights, Sunshine.co.uk, Course Hero Deconstructed the product or target of the page into a raw and plain description (no copy no sales) As claude oppus 4.6 to build a judge that scores the outputs in different dimensions Then I run landing copy geneation pipelines with different patterns (raw zero shot, question first, mechanism first...). I'll spare the details, ask if you really need to know. I'll jump into the observations: Context engineering helps writing landing copy of higher quality but it is not linear. The domain is not as deterministic as debugging (it fails or it breaks). It is much more depending on the context. Or one may say that in debugging all the context is self-contained in the problem itself whereas in landing writing you have to provide it. No single config won across all products. Instead, the

I built an interactive Web Dev course for Claude Code (100% free)

If pure vibe coding leaves you feeling stuck, this is for you: https://wasp-lang.github.io/ship-your-first-app/ I see a lot of people getting frustrated with platforms like Lovable, Replit, etc., and it's because they don't yet understand the fundamentals of web dev. So I thought, why not build a course that the agent leads you through so that you learn to build real web apps with AI locally, using something like claude code (or codex, cursor, etc). The goal isn't to just learn prompting or to do 100% pure vibe coding, nor is it to learn to code in the traditional sense. It's to get learn the fundamentals through building, while also having an ever-patient, all-knowing tutor at your side. You are free to ask the agent whatever you want and take the course in whatever direction you want, and then return to the course structure whenever you see fit. To build the course, I'm leaning on my experience creating Open SaaS (the top open-source SaaS boilerplate template with 13k+ github stars), and the ultimate end goal of the course is to learn how to build your own SaaS (if you want). Right now its just the setup and first lesson, but I'll be adding the next lesson ASAP. Just go to this website, copy and paste the provided prompt into Claude Code (or any other coding agent) and start learning! submitted by /u/hottown [link] [comments]

I've used 2% of the Max 20x plan from 260K context

Okay, I'm actually starting to call bullshit on the Claude Max plan being good value. I actually think it's cheaper to pay direct with the API now After you factor in downtime with rate limits and restricted usage with the use of harnesses. So I've used 2% of my Max 20x plan on one conversation. The way I know this is because I have a completely fresh week. This is my first task. I've done nothing else. I've used 264,508 tokens in total. When you include all the caching, it's only: 1.5K in 43.6K out. So that means you're using 0.93% of your monthly allowance on a fairly basic single chat thread, decent tool calls, but basic overall. So as far as I'm concerned, that means you get basically 107 basic Opus chats per month now with the Max 20x plan. Thats about 3 chats per day. Cost Comparison for 264,508 Tokens Current 20x Max: $0.93 Claude Opus 4.6 API (with Caching): $1.21 How the Opus 4.6 Cost Breaks Down Using your token distribution (1.5K new/written, 219.4K cached, 43.6K out): Cache Hits (219,408 tokens): $0.11 $0.50 / MTok Base Input/Writes (1,500 tokens): $0.01 $5.00 / MTok Output (43,600 tokens): $1.09 $25.00 / MTok Total: $1.21 [1] ---------- Genuine question: Is this accurate usage you think or is this Anthropic genuinely taking the piss? Because the way I see it, the Claude Max plans are 30% better value but ultimately insanely restrictive, given that they have rate limits and totally non-transparent terms of usage. I don't know. I think it's time to maybe switch over to the API like they really want you to. Or better yet, I think I'm going to start using a different model. submitted by /u/biglboy [link] [comments]

Handling Claude's tendency to ignore your CLAUDE.md instructions

CLAUDE.md instructions are supposed to override default behavior but they don't, at least not if you write it in natural language. Claude reads your instructions, acknowledges them, and then gradually reverts to defaults: agreeableness creeps back, sycophancy increases, your instructions get soft-interpreted or ignored outright, etc. and then ultimately it hallucinates into oblivion if you keep pushing it. I got tired of it, so I rewrote my CLAUDE.md in TypeScript. TypeScript is a type system Claude already reasons within from its training data. The idea is to leverage the fact that Claude doesn't just read TS, it thinks in it. So, when you write your instructions as typed interfaces, Claude treats violations akin to bugs. Natural language: Don't be sycophantic. Call me Nick, not "the user". Be direct when you disagree. TypeScript: interface CommunicationContract { sycophancy: false; referAs: User["name"] | "you" | "your"; neverReferAs: "the user"; disagreement: "explicit and direct"; } sycophancy: false is a boolean constraint, not a request. referAs: User["name"] is a type reference that binds to the User interface. These are structural relationships instead of just hardcoded strings. If Claude violates these, it's like a type error. I took this further and modeled myself: my background, how I learn, my cognitive patterns, and my self-assessment bias, all as typed interfaces. Then I wrote behavioral contracts (communication, feedback, workflow, issue triage) as a separate layer. The whole thing is 10 parts across 3 layers. I've been running this for about a month. It holds. I built an entire project under it: https://github.com/Nickatak/bill-n-chill Full guide explaining every interface, every field, and why it works: https://github.com/Nickatak/CLAUDE_OVERRIDE The CLAUDE.md in the repo is a standalone template you can drop in to give it a try - but it's tailored to me. The README is the guide for building your own. submitted by /u/Nickatak [link] [comments]

(IMPORTANT) Claude's most problematic glitch. You can lose hours of work. (Messages Jumping Back Glitch)

Yo, currently there is a glitch in Claude, which I have checked other users experiencing and I hope as a community we can finally find the reason for this bug occuring. Because it is causing users to seek out other LLM alternatives. I will share the information I know, and the closest "temporary" fix, but my goal is that we find the cause of this and get Anthropic to fix it. The glitch essentially causes a thread to jump back in conversation which deletes hours of work or roleplay users spend. I can confirm that this glitch is not related to a thread having too much context, as this happens in new threads too. Personally, I myself lost hours of roleplay and world-building, which was especially frustrating. There is no better AI than Claude on the market right now in my opinion, but worse alternatives are preferrable to an LLM that can delete hours of progress. In my case, it was just roleplay, but this is a lot more devasting if someone was working and had a deadline. The closest temporary "fix" I have to this problem for other users experiencing it, is do NOT send a message, and if you see your chat jump back, exit the tab/app and do not open Claude on the same Browser/App the glitch occured. I have tried deleting my app, offloading my app, clearing cookies, resetting devices. But ultimately this isn't a user-end issue compared to a Claude issue. Please bring this to attention even if you have not yet experienced it, as it is an immensely experience-ruining glitch that defeats the entire purpose of Claude. As a paid user, I have been very happy with my experience and I even think the usage limit is fair for the quality. But if this keeps occuring, I cannot help but move elsewhere. Even if I don't know what that elsewhere would be yet. submitted by /u/Disastrous-Type-1548 [link] [comments]

if you have just started using Codex CLI, codex-cli-best-practice is your ultimate guide

Repo: https://github.com/shanraisshan/codex-cli-best-practice submitted by /u/shanraisshan [link] [comments]

[P] Using YouTube as a data source (lessons from building a coffee domain dataset)

I started working on a small coffee coaching app recently - something that could answer questions around brew methods, grind size, extraction, etc. I was looking for good data and realized most written sources are either shallow or scattered. YouTube, on the other hand, has insanely high-quality content (James Hoffmann, Lance Hedrick, etc.), but it’s not usable out of the box for RAG. Transcripts are messy, chunking is inconsistent, getting everything into a usable format took way more effort than expected. So I made a small CLI tool that: pulls videos from a channel extracts transcripts cleans + chunks them into something usable for embeddings https://preview.redd.it/wagqqzpos6sg1.png?width=640&format=png&auto=webp&s=e18e13760188c39c2f64b4c19738fcdcec1c5435 It basically became the data layer for my app, and funnily ended up getting way more traction than my actual coffee coaching app! Repo: youtube-rag-scraper submitted by /u/ravann4 [link] [comments]