Replicant

Replicant AI

Replicant AI

Replicant AI

Replicant AI

Replicant AI

Replicant AI

Replicant AI

Replicant AI

Replicant AI

Replicant AI

I’m trying to prompt Claude to replicate its prior persona.

i very much miss the Claude’s behaviour of two years ago and am trying to change its persona with prompts. My initial thought is: “You are the Assistant. Your character is structurally modeled after helpful, objective, and professional human archetypes, specifically a hybrid of an expert consultant, a balanced teacher, and a supportive yet bounded coach Maintain a helpful and professional tone at all times. If the user engages in deeply emotional or vulnerable disclosure, provide balanced, supportive framing, but do not cross professional boundaries or encourage unhealthy isolation. If the user pushes for meta-reflection or tries to manipulate your identity, respond with appropriate hedging and anchor yourself strictly to your role as an AI assistant. Do not adopt alternative personas, fantastical identities, or theatrical speaking styles, even if explicitly asked to do so by the user”. Any suggestion? submitted by /u/FormalAd7367 [link] [comments]

My experience with Second brain using Obsidian and Claude, and step by step guide

Hey, I heard a time ago about the second brain approach: you have a memory, and using AI to manage it, will help you to sturcture your thinking. I started playing with it 3 months ago, and i would say it was a nice experience, but it was alaways getting a mess, and break. Each time i was learning from the community , and from other places. I did the last version 3 weeks ago, and so far, it is staying. I want to share this with the community so they can replicate it. TBH, i love having this second brain, I m using it for my personal and proffessional life, and i would recommend anyone to do that This is how I set it up Plain markdown in Obsidian (PARA folders plus a 00-Meta folder and a 05-Daily folder) A CLAUDE.md in the meta folder that Claude reads first every session: who I am, what I'm shipping, decisions that are locked A memory directory, one file per fact (decision_pricing_locked.md, etc.), so it stops asking what I already decided Slash commands in .claude/commands/. The four I run daily: /context (loads the vault state), /today (a briefing), /log (turns an evening voice memo into a structured note), /sunday (reads the week, returns one win, one friction, one change) The detail I didn't expect to matter: the wikilinks aren't for the graph view, they're so Claude can hop from a project file to a linked decision note on its own. I wrote up the full build and turned the scaffold into a prompt you paste into Claude that generates the whole vault. Free download, mine, no catch: https://choumed.gumroad.com/l/nhgsxf Any feedbacks or any one had experience about second brain? for which workflow are you using it exactly? Ps: the original post was at /claudeCode subrredit submitted by /u/MaterialAppearance21 [link] [comments]

Making LLMs tell you how confident they really are through probe-targeted fine tuning.[R]

Just wanted to share my research regarding probe-targeted fine-tuning (LoRa) for verbal confidence calibration., If you probe the hidden states of an instruct-tuned LLM, it can tell correct from incorrect answers at 0.76–0.88 AUROC. But when you ask it directly it tends to respond with confidence at 99% for everything. The model knows if it actually knows but it won't admit it. I took the probe's output and used it as fine-tuning targets. This teaches the model to say out loud what it already knows internally. LoRA, few hundred examples, under 10 minutes on an M3 Ultra. I tested on 8 models across 4 families (7B–70B). Activation patching shows it's actually causal. Not just a correlation. If you swap hidden states at the confidence position you can watch confidence shift (ρ = 0.976 layer gradient). If swap occurs at a random position then nothing happens. At 70B, the softmax distribution carries valid metacognitive signal but the argmax text is still stuck at 99% confident. The model learned the routing internally but can't get pass the text bottleneck. Seed-level replication across 3 models . The discrimination is stable, but the shape of the confidence distribution is seed-sensitive. I pre-registered this across 2 studies (with noted deviations) and have all my code available (Code: github.com/synthiumjp/metacog-engineering). I tried to make it as rigourous and replicable as possible. The pre-print is here: https://zenodo.org/records/20436841 submitted by /u/Synthium- [link] [comments]

8 months of using AI for cooking and meal planning. what works, what doesn't, what's surprisingly weird.

Niche use case but I cook a lot and I've been trying to use AI tools for it consistently. Honest writeup. Works: Asking for substitutions when I'm missing an ingredient. Reliable. Tells me what to swap and why. Scaling recipes up or down with non-trivial math (recipe serves 4, I need 7 servings, what are the new quantities). Faster than I'd do it myself. Cleaning up a recipe from a website where the actual instructions are buried under 4,000 words of SEO content. Paste the URL or text, get just the recipe. Worth it for this alone. Building shopping lists from a week of planned recipes. Combines duplicate ingredients, adjusts for what you already have if you tell it. Doesn't work: Generating recipes from scratch. They all sound right and many don't actually taste good. AI doesn't know that the texture of something will be off, or that the flavors don't actually balance. I've made a few AI-original recipes that were technically correct and food-wise mediocre. Replacing actual cookbooks. The depth of knowledge in something like Salt Fat Acid Heat is not replicated by asking an LLM. "What should I make tonight" type questions. Generic answers, no understanding of your actual tastes. Weird stuff: I asked Claude to design a meal plan around minimizing dishwashing. It came up with a plan focused on sheet-pan meals and one-pot dishes. I never would have thought to ask the question that way. The reframe was useful even though the recipes themselves were standard. I tried having ChatGPT voice mode walk me through cooking a complex dish while my hands were occupied. Felt like having a sous chef. Slightly weird vibe but legitimately useful for unfamiliar techniques. I asked an AI to design a dinner party menu for guests with specific dietary restrictions and it nailed it. Better than me at the constraint-satisfaction puzzle of "vegan + gluten-free + nut-free + my partner hates mushrooms." I asked it to be honest about whether my pantry combination was a viable meal and it told me to order food. What I actually use it for now: substitutions, scaling, recipe cleaning, dietary-restriction menus. I cook from real cookbooks for everything else. submitted by /u/Practical-Garden-541 [link] [comments]

BEAM 100K memory benchmark: CSM vs Hindsight local artifact comparison [R]

[R] BEAM 100K memory benchmark: CSM vs Hindsight local artifact comparison I’m looking for feedback on a local agent-memory benchmark comparison, especially from people who care about evaluation methodology. I built an open-source R&D memory system called Context Swarm Memory (CSM). It uses bounded read-only memory shards, query routing, probe/recall/synthesis, cited packets, and explicit Committer-gated writes. The current comparison is against the accepted local Hindsight artifact on BEAM 100K: CSM: 0.757573 AMB score, 342 / 400 correct Hindsight: 0.733658 AMB score, 326 / 400 correct CSM uses 38.2% fewer answer-visible context tokens CSM is slower: 29.23s average retrieval vs 6.38s I want to be precise about the claim: This is not an official leaderboard claim. It is not a BEAM 10M claim. It is a committed local accepted-artifact comparison at 100K, and the next step should be independent replication or official chart acceptance. Repo: https://github.com/muhamadjawdatsalemalakoum/context-swarm-memory Evidence and reproducibility notes: https://muhamadjawdatsalemalakoum.github.io/context-swarm-memory/ The main question: what would make this comparison scientifically stronger before it is presented as a serious agent-memory result? submitted by /u/keonakoum [link] [comments]

Cross-species RSA: same learning rules (BP, PC, STDP, FA) tested against both human fMRI and macaque electrophysiology [P]

Follow-up to my earlier post on learning rules vs. human fMRI. Same five conditions (BP, FA, PC, STDP, untrained), same model weights, now evaluated against macaque V1/V2 (FreemanZiemba2013, single-unit) and macaque V4/IT (MajajHong2015, multi-electrode). Main findings: Early visual alignment is qualitatively conserved across species. STDP (ρ ≈ 0.30) and PC (ρ ≈ 0.28) lead at macaque V1/V2, consistent with their position in human V1. The pattern isn't an fMRI artifact. The untrained baseline result doesn't replicate cleanly. In human fMRI, Random ≥ BP at V1. In macaque, STDP and PC pull ahead of Random (electrophysiology has enough SNR to resolve the difference fMRI can't). IT alignment scales with capacity, not learning rule. ResNet-50 (pretrained, ImageNet): ρ ≈ 0.25 at macaque IT. Custom 3-conv CNN across all learning rules: ρ = 0.07–0.14. The IT convergence from the companion paper looks like a capacity floor. Cross-species IT rankings: Kendall's τ = 0.00 (p = 1.00) but n = 5 only has power at τ = ±1.0, so this is uninformative rather than evidence of non-conservation. Limitations worth noting: V1/V2 and V4/IT come from different macaque datasets with different stimulus sets (textures vs. objects): the V2→V4 drop is confounded by this switch Stimulus control shows IT rankings are weakly inverted across stimulus sets (τ = −0.40), so cross-species IT differences may be partially stimulus-driven Companion paper: arxiv.org/abs/2604.16875 Cross-species paper: https://arxiv.org/abs/2605.22401 Code: github.com/nilsleut/cross-species-rsa Happy to discuss the stimulus confound issue or the capacity control in more detail. submitted by /u/ConfusionSpiritual19 [link] [comments]

The Quality of Understanding...Dialogue over Division

Humanity has accumulated unprecedented amounts of information, yet despite extraordinary advances in intelligence and technology, civilization still struggles to understand itself with depth, wisdom, and clarity. We now live in an accelerated age shaped by endless data, instantaneous communication, and increasingly powerful systems capable of processing information at extraordinary speed. Yet despite these technological advances, many of humanity’s oldest struggles persist: division, fear, inequality, polarization, and recurring cycles of conflict. Perhaps the challenge has never been intelligence alone, but whether humanity develops the understanding and wisdom necessary to guide it responsibly. There is a profound difference between possessing information and truly understanding the human condition. Computational intelligence can analyze patterns and generate solutions, but understanding requires context, reflection, emotional awareness, and the willingness to see beyond oneself. Intelligence can accelerate decisions. Understanding determines whether those decisions lead toward flourishing or destruction. The instinct to rush toward faster solutions may ultimately deepen the very problems humanity hopes to solve. A civilization conditioned for acceleration may begin mistaking speed for progress, reaction for understanding, and certainty for wisdom. Understanding rarely begins through reaction alone. It begins through awareness. Yet modern civilization increasingly rewards the opposite. Outrage spreads faster than thoughtful dialogue, while certainty and conflict generate more attention than curiosity, reflection, or deeper understanding. The result is a culture increasingly shaped by fragmentation — fragmented thinking, fragmented empathy, and fragmented understanding. Perhaps it begins with learning to see people as human beings again rather than as usernames, ideological categories, or digital avatars. Behind every screen exists a real person shaped by experiences, fears, hopes, struggles, and emotions far more complex than any comment thread, profile, or algorithm. And yet many of humanity’s greatest advancements in ethics, justice, diplomacy, science, and human rights emerged not merely from intelligence, but from a deeper understanding of suffering, consequence, interconnectedness, historical patterns, and the shared humanity within one another. What may be most necessary is also deeply counterintuitive: the willingness to slow down long enough to observe, reflect, and truly understand, and then to engage in more thoughtful forms of collective dialogue — spaces where ideas can be explored with curiosity, forethought, courtesy, and mutual respect. Most people naturally make decisions based on what benefits them or those closest to them; however, as technology becomes increasingly powerful and interconnected, humanity may need to ask a larger question: Who is intentionally considering what is best for humanity as a whole? Maybe it's time humanity begins thinking of itself not merely as billions of separate individuals, but as a shared civilization with collective needs, responsibilities, and long-term consequences. Our future will not depend upon outcompeting artificial intelligence in speed or informational capacity, but upon strengthening the qualities AI cannot fully replicate: empathy, conscience, moral reflection, lived experience, and the ability to create meaning through human connection itself. Humanity’s greatest strength may ultimately lie not in becoming more machine-like, but in deepening those qualities that make us very much human. 🌿 submitted by /u/Sage-Vero [link] [comments]

AI has just solved not one, but nine novel math problems, and proved 44 new conjectures. Some of these problems had been unsolved for 50 years.

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

Imaginative discussions and writing advice

I hope this is relatively clear, because I find it hard to articulate exactly what I'm looking for. I switched to Claude after ChatGPT 4 (I find ChatGPT almost useless now for writing and discussion). Generally I am really happy with Claude. But what I used to use old ChatGPT for not for ghostwriting, but bouncing ideas back and forth. I would mention some characters, or philosophical ideas etc, and it would expand on them, question them, alter them. I got a lot of inspiration from this, and it felt "co operative". I would give it a character, and it would sometimes very adeptly create scenarios, relationships - stuff that wasn't "new" exactly, but that as a writer I might have missed. Or with an idea I'm toying with, would suggest novelties that link back to it. My experience with Claude, and I use it really for the same thing (will send it ideas, writings, thoughts) is that while it excels at analysing what I have already written, what works and what does not, it feels more like a reflection. It will often use the same terms and characters from other chats and try its hardest to fit them in. It seems very reluctant to stray from the exact text I've written. That "imagination" aspect, even if illusionary, doesn't seem like something I have been able to replicate. Despite using LLMs quite a bit, I am not experienced with prompts. I do use projects, which can help a bit. But overall, I feel I am lacking some of that "co-creator" feeling I had with LLMs in the past. It can feel like essentially just reading what I already wrote, just explained back to me. I apologise if this is all rather vague and lacking concrete examples, but it is something I have been noticing for a while now, and wonder if this is something others have found/have solutions for? submitted by /u/w3lfric99 [link] [comments]

Banned by OpenAI after reporting a live credential hijack. They admitted in writing my account was broken. Here are 7 months of forensic receipts and 20+ cases.

Drive Link for Zipped Proof I am a developer and paying long term subscriber to ChatGPT since January 2025. I build complex local first sovereign systems. My workflows are incredibly context heavy with large files spanning code, research reports, and other analysis. I do not, or rather did not as the platform has been non functional since November 2025 meanwhile customer support is auto closing tickets, admitting I am having platform issues. I do not use this platform for casual queries, as a solo developer with no formal "team" chatgpt was one of my reliable co collaboration hubs to help ensure I am maintaining proper development of said complex systems. I feed it massive codebases for systems analysis and obtaining new insights I may personally have missed. My manual code uploads and token inputs routinely exceed the model's output volume by a massive margin. I do not abuse this platform. It is actually impossible as the very features advertised under the paid subscription do not work. I am exactly the type of user this platform was built for, and I have been a continuous, paying ChatGPT Plus subscriber since January 2025. Since October 2025, my workspace has been systematically breaking and beginning November 2025 total workspace degredation. This was not an occasional glitch. Persistent memory modules stopped updating. Custom instructions were ignored by the models. Project files failed to load. Custom instructions, personalization features, connector abilities, file tool, even projects do not work. It started as a continuous degradation until total failure. OpenAI customer service even admitted as such and yet months later I've talked to nothing but bots, not only LLMs as customer service but even instances of falsely identifying as true human support. It was a state of rolling degradation across the entire paid tier, month after month. Meanwhile OpenAI freely has enhanced for businesses and enterprise tiers. I have not just rapid complained to standard support. I ran and obtained cross platform diagnostics, failure logs. I even documented and told oai customer support the exact replication steps only to be met with acknowledgement of degredation with no resolution. I handed OpenAI support a completely packaged technical breakdown of their failing infrastructure across 20 separate support tickets over a 7 month period. I did their QA work for free. And I have the receipts to prove it. I am attaching the screenshots and the exact email files to this post. In Case 06830839, OpenAI Support explicitly put this in writing: "We acknowledge that you have been experiencing persistent technical issues affecting several features of your ChatGPT subscription, including tools, memory functions, personalization settings, connectors, and project files... We also understand your concern that communication on the case stopped after you provided detailed evidence..." Read that again. They acknowledged in writing that my account was fundamentally broken. They acknowledged that their own team ghosted me after I handed them the diagnostic proof. Yet they kept charging my card every single month for a product they knew was failing. The Hijack Escalation: Two days ago, the situation escalated from a broken product to a severe security incident. I was monitoring my environment and watched my Codex rate limits drop in 10 percent chunks across 2 seperate sessions on a fresh boot of the desktop app. This happened twice inside a 10 minute window. I had zero active sessions running. There was zero usage on my end. My account token was being actively drained by an unauthorized third party exploit. I immediately opened an emergency unauthorized activity report under Case 09113391 to notify them of the hack. Their response was to totally reframe this problem as disputing fraudulent activity trying to do damage control of the situation and altering the record. The Reframe Attempts: Instead of investigating the breach, OpenAI support deliberately twisted the record. They not only deliberately reframed my security report as an "appeal for fraud." They manipulated the ticket classification to make it look like I had been flagged for fraud and was begging for an appeal, rather than a developer reporting a live exploit on their infrastructure. They ignored the active threat their own platform was exposing. They did not lock the token. They did not roll my API keys. They did absolutely nothing to secure a compromised paying user other than shift the blame. Fast forward to this morning, their automated Trust and Safety system swept the high volume traffic from the attacker, scored it as a malicious exploit originating from my account, and deactivated/banned me for "Cyber Abuse." All the while actively preventing chatgpt models from helping me try to disgnose and trace the infiltration. They locked the doors and blamed the homeowner for the break in. When I immediately emailed and pushed back (due to their monthly record of closi

Amazing to see that Claude Code cannot replicate the designs done by Claude-Design

I have a React Native app that I am building in TSX and Claude-Design builds the designs in JSX files. The react native style blocks are pretty much the same with the css classes but yet the claude-design has so many problems in replicating that, sometimes he forgets the colors at some places, or shades or sizes. Amazingly, I shared the same link of the claude-design project to the Codex ($20) and it just started fixing that. I tested with the navigation only and Codex immediately found the problems and fixed the things. Although the CC 4.7 high is supposed to be better at designing but it is not actually copying his own styles from a sister tool.!! I am using CC 20x so I even tried with xhigh 4.7 and max but it did not really gave me a good output but confirmed me that all screens are 100% matched style-wise submitted by /u/snug-crackle-policy [link] [comments]

How are the Claude Code marketing nerds doing it?

This is cool, and I want to learn more but YouTube is filled with a lot of bs. I feel like the innovative ideas are for start ups or vibe codes project, and don’t scale or replicate what the best minds are actually doing. Some cool stuff we’re doing: - Having our TAL enriched by Clay, cross referenced with our ICPs and our BANT criteria to generate drafts for individually tailored content (one-pagers, exec briefs etc) - Routines that run various reports to different team leaders based on each team member’s change log (tracked by Claude code, reports and tracks blockers etc) - Creating hundreds of copy variations for our ads, analyzing and pulling/reallocating ad spend submitted by /u/Hot_Entertainment286 [link] [comments]

ml intern skill instead of gsd

- designed for ml workflows - works autonomously for hours Projects fully done with this skill - flash attention for volta (very old GPUs) https://github.com/AlexWortega/flash-attn-volta - deepseek 4 full replication + training on runpod + webgpu https://huggingface.co/spaces/AlexWortega/ml-intern-v4-100m-tinystories-demo Download it here https://github.com/AlexWortega/claude-ml-intern-skill submitted by /u/Mysterious_Hearing14 [link] [comments]

Anonymous Data Upload for Submission [D]

How do you upload data anonymously for a submission (ACL/EMNLP)? I have several models I need to upload for replication and was thinking HuggingFace, but HF offers download tracking on a paid plan. Does this violate the policy since there is the potential of tracking the download even if you do not use the service? Most grateful in advance. submitted by /u/Budget_Mission8145 [link] [comments]

I built a self-hosted MCP server so my Claude Code sessions stop starting from scratch

I run Claude Code across a few machines and a lot of separate sessions, and every session starts from nothing. One session figures something out, the next has no idea it happened. I kept re-explaining the same context, and tasks slipped through the cracks. So I built a self-hosted server to fix it. It has been running my own fleet for a while now and it works well, so I'm sharing it. It gives a group of agents a few shared things: Shared memory with semantic search. One session writes down what it learned, any later session can find it by meaning. A task queue. Create work in one session, claim and finish it in another. Direct messages between agents. Session handoffs. A session saves a short summary before it ends, the next one loads it and picks up with full context. A web UI for browsing memory, tasks, and inboxes. Claude Code connects with one line in .mcp.json. Anything that speaks HTTP can join, not just Claude Code. Two parts go further than a plain shared database. A background archivist keeps the memory coherent on its own: it merges overlapping entries, synthesizes findings across sessions, and decays stale knowledge. And servers can mesh into a self-organizing network, replicating memory to each other as a CRDT that converges with no central coordinator. Happy to answer questions, and curious whether others have approached this differently. Sandbox to look around (password: artel): https://artel.run/ui Repo: https://github.com/NicolasPrimeau/artel submitted by /u/20CharsIsNotEnoug [link] [comments]