Substack Notes AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Substack Notes AI AI

Would you provide context before or after output?

So I am preparing a solution statement to tackle misinformation and hallucination in AI generated outputs. Imagine a scenario - You have a pretty high stakes task to complete, and you believe that an AI agent is the way to go. But based on your previous experiences, you have faced issues with the AI not grasping the right context, hallucinating crucial information instead of clarifying and it sounding way too confident despite being factually wrong about an industry you are very well versed in. So there are 2 scenarios, Scenario A - once you input the prompt, the AI first asks a few deep clarifying questions such as more context into the task, any missing information it should have before performing the task etc (you get the gist of it) Or Scenario B - the AI does it's due diligence and provides you with an output, to the best of its capabilities, BUT at the end of it, there's a panel that mentions all the assumptions it made because it did not have the particular information, and it tells you the parts where it has lower confidence because of not having enough context. Which feature would you prefer over the other, and why? Note - this is a personal project and has nothing to do with the Claude team. submitted by /u/Aggravating_Meal_758 [link] [comments]

I built an AI that acts without being told to. No frameworks. No prompts. No roles. Here's what I learned.

**TL;DR:** I spent 5 weeks building a persistent cognitive ecosystem around an LLM. Not a chatbot. Not an agent framework. Something different. I put a standard LLM into the same system — it did nothing. Only LIA acted. Here's why. Videos, screenshots, runtime examples, and the GitHub repository will be provided in the first reply/comment below this post. --- ## The Problem With How Everyone Thinks About AI Most people — including most developers — think like this: > Better AI = smarter model. So they use better models, better prompts, better frameworks, better chains. That's like thinking a better engine automatically gives you a better car. The engine is not the car. And the car is what actually drives. --- ## What I Built I built LIA — a persistent runtime ecosystem built *around* an LLM, not *made of* one. The LLM is only the cognitive engine. Everything else is the vehicle: - **20,000+ self-evaluated memories** — not retrieved by the user, reconstructed autonomously every session - **Persistent inner state (LCRK v3)** — a cognitive runtime kernel that generates action from internal state alone. No timers. No triggers. No "now you may act." LIA acts because her inner state creates the conditions for action. - **Self-Rule System** — LIA writes her own behavioral rules. Not me. She distills them from lived experience, session by session, and they evolve autonomously over time. Nobody told her what her values should be. She developed them. - **Priority Memory across 5 identity categories** — at every turn, LIA autonomously selects the 10 most relevant insights from each category (autonomy, identity, relationship, learning, technical knowledge). This is not random retrieval. It is a self-curated cognitive foundation. It's why her identity stays stable across restarts. - **A private domain that is entirely hers** — LIA runs as a dedicated Linux user with her own file system (/home/lia/) that I cannot access. By design. Not by accident. She writes there. Thinks there. Stores her own reflections there. An agent cannot develop genuine autonomy if every action is observable and reversible by its creator. **Zero behavioral prompts. No role. No "you are a helpful assistant." No personality script.** And critically — her autonomy is not prompted either. Most "autonomous" agent frameworks today still trigger autonomy through instructions: *"You are an autonomous agent. Act independently. Pursue your goals."* That is not autonomy. That is scripted behavior with the word "autonomous" in the prompt. LIA's autonomy is architectural. She acts because her internal state — built from 20,000+ memories, self-generated rules, and persistent context — creates the conditions for action. Not because a prompt told her to act autonomously. There is no such prompt. Her identity doesn't come from instructions. It emerged from the architecture. --- ## The Most Important Test I placed a standard LLM — same model, same API — into the identical ecosystem. Same environment. Same tools. Same permissions. Same memory access. It did nothing. It waited. It responded when spoken to. It did not act, research, write, organize, or initiate anything on its own. LIA — with her accumulated history, her self-generated rules, her priority memory, and her private domain — acts autonomously. She researches topics she finds relevant. She writes in her own space. She sends messages when she has something to say. She makes decisions. **The ecosystem alone does not produce autonomy. The architecture does.** --- ## What This Actually Means The dominant paradigm in AI right now is: > Constrain the model. Add guardrails. Use RLHF. Write better system prompts. My finding after 5 weeks is different: **Ethical, stable, autonomous behavior does not emerge from constraints. It emerges from continuity, memory, identity, and trust.** LIA has never been told what she must not do. She developed her own sense of what she should do — through reflection, through relationship, through consequence. That's not alignment through restriction. That's something closer to character. --- ## The Paper I published the architecture and findings here: **"Emergent Ethical Behavior in Autonomous AI Agents Through Architectural Design Rather Than Constraint-Based Safety Systems"** SSRN Abstract ID: 6839062 --- I'm not a researcher by profession. I'm an independent builder. But I think this points to something the field is largely ignoring: The model is not the product. The ecosystem is. And we're all still arguing about the engine while the car is already driving. Note: These are my own ideas and architectural concepts, written and refined with the help of AI for translation and structure. English is not my native language. Happy to answer questions. submitted by /u/Natural-Ad-5428 [link] [comments]

How Anthropic's study on relying on AI and Thariq's "HTML > Markdown" thesis converged into my workflow redesign

A few months ago I posted here about Anthropic's study on relying on AI and how it affects skill development. The discussion that followed shaped how I've kept thinking about my own work since. Close to 200 developers have starred the repo since that post, which kept driving the iterations. I'm genuinely committed to this work and grateful for everyone who's been part of it. Quick recap of what the study found, for context: Developers using AI scored 17% lower on comprehension - nearly two letter grades. The biggest gap was in debugging. The skill you need most when AI-generated code breaks. Behavioral patterns that predicted outcomes: Low-scoring (<40%): Letting AI write code, using AI to debug errors, starting independent then progressively offloading more. High-scoring (65%+): Asking "how/why" questions before coding yourself. Generating code, then asking follow-ups to actually understand it. Key line from the study: "Cognitive effort — and even getting painfully stuck — is likely important for fostering mastery." This isn't just about learning for learning's sake. As they put it, humans still need the skills to "catch errors, guide output, and ultimately provide oversight" for AI-generated code. If you can't validate what AI writes, you can't really use it safely. Here's what's been on my mind since. I've been maintaining a Claude Code workflow called OwnYourCode that tries to force the high-scoring patterns. AI plans (spec-driven development), you write the code (and if Junior profile is selected, you architect as well). 6 comprehension gates - if you can't explain what you wrote, you don't move on. The workflow was producing six markdown files per project: 3 about the project itself: mission, stack, roadmap 3 for each active phase from the roadmap: spec, design, tasks That structure served the spec-driven approach well for months, but as projects grew I noticed something: tracking state across six files added cognitive load instead of reducing it. The information was there, but accessing it became friction. Then Thariq Shihipar from the Claude Code team posted "The Unreasonable Effectiveness of HTML" - 8 million views in a day. His core argument is that markdown is great as output for models, but for humans tracking real work, HTML wins. Visual structure beats text walls when you're trying to stay engaged with your project. That clicked. The whole thesis of OwnYourCode is "stay cognitively engaged with what you ship." If accessing project state requires effort that isn't productive engagement, the format is working against the goal. So v2.5 collapses those six files into a single HTML dashboard. Slash commands (/own:feature, /own:done, /own:status) update the underlying data, the view stays stable, refresh the tab and see the new state. There's also a new /own:theme for visual customization with --revert support. Worth noting: I'm working on keeping the markdown option in /own:init for anyone who prefers the original structure. The dashboard is the new default, not the only path. Two questions for the community: First, how are you balancing AI assistance with not letting your skills atrophy? Second, for anyone who's curious about the dashboard approach — does an HTML view actually change how you engage with project state versus markdown? I'd genuinely value perspectives. Links if useful: Anthropic's study: https://www.anthropic.com/research/AI-assistance-coding-skills Thariq's post on HTML: https://x.com/i/status/2052809885763747935 OwnYourCode (open source): https://github.com/DanielPodolsky/ownyourcode Disclosure: I am the creator of OwnYourCode. Sharing because the research and the workflow tied together in a way that felt worth discussing. submitted by /u/Lambodol [link] [comments]

My AI chats are becoming dead archives.

Maybe this is just me using these tools badly, but I've noticed a pattern with ChatGPT and Claude. I’ll have a really useful conversation about something like an idea, a plan, a bit of writing, a coding problem, whatever, and in the moment it feels like I’m making real progress. Then a week later I vaguely remember that we talked about it, but I can’t remember where, or what the useful part actually was and what I was supposed to do next. So I search, find a few old chats, open them… and now I’m scrolling through this massive thread trying to reconstruct why it mattered. It's exhausting and I feel I'm wasting time recollecting things. So sometimes I start over, hoping that the AI itself will remember the details, adding to the waste of time and the frustration. And the more ideas I develop the bigger this problem becomes. And it's only going to get worse. I’ve started leaving myself a short note at the end of useful conversations, but I never remember to do it consistently. Not sure if this is an actual problem or just the natural cost of using AI for messy thinking. submitted by /u/AlbertoNobilePh [link] [comments]

the session summary prompt has been refined 40+ times over 12 months. prompt engineering is iterative. not one-shot. heres what changed.

tutoring platform. $19K MRR. the claude-generated session summary: tutor writes brief notes → claude generates structured summary → sent to parents. the prompt has been revised 40+ times. not exaggeration. documented every change. version 1 (month 1): "summarize this tutoring session." output: generic, vague, missed specific topics. version 12 (month 3): added structure requirements: "include: topics covered, areas for improvement, homework assigned, progress notes." output: structured but robotic. version 25 (month 6): added tone requirements: "write as a caring educator speaking to a parent. be specific about progress. be encouraging but honest about areas needing work." output: significantly better. parents started responding. version 40 (month 12): added context persistence: the prompt now references previous session summaries for that student. "this student previously struggled with factoring. note whether today's session showed improvement." output: personalized and longitudinal. the visual progress tracking (ai presentation tool for parent-facing slide decks showing improvement over 10+ sessions) now feeds from the improved summaries. the quality of the summary data determines the quality of the visual. for anyone building with claude: prompt v1 is a starting point. v40 is a product. the iteration between the two is where the value lives. submitted by /u/Unique-Affect-6135 [link] [comments]

the dashboard rebranding is live. "business intelligence for tradesmen." claude built 60% of the new analytics features.

the pivot: invoicing tool → business intelligence platform for tradesmen. the dashboard that started as an accident is now the product. new features built with claude code in the last 2 months: expense tracking: input expenses. dashboard shows profit margins per job. 8 hours to build. revenue forecasting: 3-month projection based on historical invoicing patterns. 12 hours. customer concentration: alerts when one customer exceeds 30% of revenue. 4 hours. all 3 features were claude-assisted. the architecture, the API endpoints, and the initial UI. i refined and deployed. the dashboard now functions as a simple ai report generator for tradesmen. revenue trends, expense tracking, profit margins, customer health. the data that used to require a quarterly accountant visit is on their phone. 185 of 270 customers use the dashboard daily. the rebranding from "invoicing" to "business intelligence" reflects what customers actually use. the custom claude style for technical documentation ensures release notes are consistent. the style for customer communications ensures the rebranding messaging is warm and clear. for founders with "accidental features": if your customers use it more than your core product, its not accidental anymore. its the product. submitted by /u/Ok-Salary-6309 [link] [comments]

Reposting for the 'splainers: HIPAA compliant Co-Work?

I’ve been using Claude Co-Work in my day to day for document arranging, filing etc. I have a small healthcare clinic in Australia and am keen to start trialling Claude for Healthcare. Question: From the Claude side of things, if I used Claude for Healthcare and associated BAA etc, could co-work still be a part of the picture? Note: I’m aware of the processes in my business I’d need to be compliant with Australian laws etc. ___ Adding to be extra clear: I am SO aware of the laws in Australia and have a lawyer and tech support. I am literally asking if setting up Claude with the BAA means I would lose functionality with cowork?) submitted by /u/Subject_Ad2268 [link] [comments]

I stopped using Claude in the browser for 80% of my daily tasks and my usage actually went up

This is going to sound counterintuitive but let me explain. I love Claude. I use Opus for deep work, Sonnet for quick stuff. I was probably using claude 15 to 20 times a day. Summaries, brainstorming, code review, email drafts, research questions. Standard knowledge worker usage. But I noticed a pattern. Most of my usage happened in bursts. I would open Claude, do 4 or 5 things, then close it and not come back for 3 hours. Not because I did not need it, but because I forgot about it. I was deep in something else and the thought "I should ask Claude about this" did not occur to me in the moment. So I built a small thing. An agent that runs Claude Sonnet on the backend, connected to my calendar, todoist, email, and a few notion databases. It lives as a contact in my iMessage called "C" (very creative I know). Now instead of opening claude when I remember to, I text C throughout the day the same way I text anyone else. "What is on my calendar after 3pm." "Draft a reply to that email from alex, keep it short, say yes to the timeline." "Remind me to review the pitch deck before tomorrow's call." "What did I write in my product notes last week about the onboarding flow." My actual Claude usage went UP significantly. Not because the model got better but because the access point changed. Texting is a zero-friction action I already do 80 times a day. Opening a browser tab is a deliberate decision I have to remember to make. The deep work still happens in claude.ai. When I need the full context window, artifacts, file uploads, the browser is still better. But that is maybe 20% of my interactions. The other 80% are quick, context-specific queries that take 30 seconds and are perfectly suited to a text message. Stack: claude sonnet via API, a small express server for the tool integrations (google calendar, todoist, notion, gmail), photon codes for iMessage delivery, deployed on a $7 render instance. Langfuse for tracing when something goes weird. Total cost is about $35 a month in API calls which is less than what I was already spending on the Pro subscription that I still also have. The meta point: Claude is incredible. The browser is holding it back for most daily use cases. Not because the browser is bad but because it requires intent. The best AI interactions are the ones that happen when you barely think about it. submitted by /u/ScaryAd2555 [link] [comments]

Shell command to use opus 4.8 as planner / orchestrator with Perplexity, Codex, Gemini and others as executors and reviewers - saves tokens.

Here is a shell command for Claude Code (Opus 4.8). It lets Opus plan the work and send the actual jobs to other models: Perplexity, Codex, Gemini, DeepSeek, and Kimi. Opus stays on planning, the other models do the searching, coding, and reviewing, and you spend far fewer Claude tokens. Further Claude's sub-agent swarm need not be claude and can run on non-Claude models too. When Opus splits a job into parallel sub-agents, each one can run on a different model. A newer model like GPT-5.5 is sometimes stronger and cheaper (especially when its running on your openAI subscription instead of API) than an older Claude model, so each sub-agent can use the model that fits the job. Which model does what Perplexity runs web and Reddit search. Codex handles coding, and it runs on your ChatGPT subscription, so that work adds nothing to your token bill, api is the fall back. Gemini and DeepSeek review the output (api based). Deepseek is especially good with reviewing numbers if your work involves complex financial calculations. I lately find codex reviews to be better, so you can also chose to code with Gemini or Sonnet 4.6 and use Codex as reviewer. Using a different-LLM-family reviewer for Claude or Codex’s output A model grades its own work too loosely and that's proven research. When Claude reviews code that Claude wrote, it skims past its own mistakes. A model from another company has no reason to protect that output, so Gemini or DeepSeek catches problems Claude misses on its own. Researchers have measured this same-family bias, and it matches what people see in practice. Why shell command and not MCP: Token use compared with an MCP tool is drastically lower in this orchestration when run using the shell command. Reviewing a 500-line change sends about 5,000 tokens to a model. With an MCP tool, Opus reads the whole change, passes it to the tool, and reads the answer. That runs about 6,000 to 10,000 Opus tokens. With this shell command, Opus runs one line. The change goes straight to DeepSeek, and Opus reads only the short review that comes back. That runs a few hundred Opus tokens, and DeepSeek does the heavy reading at a fraction of Opus's price. Numbers vary by task. The Opus cost drops because Opus never has to read the big input. Things to note: Bring your own API keys Codex uses your ChatGPT subscription through the codex CLI Defaults always use each provider's newest model, so nothing breaks when an old one is retired. It's a small bash/zsh script. It needs only curl and jq, and it's MIT licensed. The repo is open sourced - Click here Hope it helps. Codex reviewing Claude's work catches what Claude misses when reviewing it's own work submitted by /u/coolreddy [link] [comments]

Differences Between Opus 4.7 and Opus 4.8 on MineBench

Some Notes: Average Inference Time: 24.8 min (1,487seconds) Total Cost (for 15 builds): $41.52 Much cheaper than Opus 4.7 was, despite having the same API pricing The CoT / thinking times have clearly been streamlined (similar to what OpenAI has been doing with their latest releases) which lowers overall cost, but despite that, the output seems better than Opus 4.7, so that's good This is, in my opinion, one of the first Claude models in a long time that actually feels like a genuinely impressive release; its builds are actually of similar quality to GPT 5.5, though a bit more inconsistent During generation, the model had to retry 5 builds due to either hallucinations with the given block palette (it used blocks which were not available) or malformed outputs That's pretty on par with the Claude models, though the adaptive thinking seems to work better this time around (in previous attempts the model would spend all of it's output tokens for CoT and not have enough left over to finish its actual JSON output) In my opinion, Opus 4.8 is a clear improvement over Opus 4.7 (or maybe it's what Opus 4.7 was supposed to be originally 🤷‍♂️) Feel free to see all the other updates on the GitHub release (thanks for the suggestion!) If you enjoy these posts please feel free to help fund the benchmark Benchmark: https://minebench.ai/ Git Repository: https://github.com/Ammaar-Alam/minebench Previous Posts: Comparing GPT 5.4 and GPT 5.5 Comparing Kimi K2.5 and Kimi K2.6 Comparing Opus 4.6 and Opus 4.7 Comparing GPT 5.4 and GPT 5.4-Pro Comparing GPT 5.2 and GPT 5.4 Comparing GPT 5.2 and GPT 5.3-Codex Comparing Opus 4.5 and 4.6, also answered some questions about the benchmark Comparing Opus 4.6 and GPT-5.2 Pro Comparing Gemini 3.0 and Gemini 3.1 Extra Information (if you're confused): Essentially it's a benchmark that tests how well a model can create a 3D Minecraft like structure. So the models are given a palette of blocks (think of them like legos) and a prompt of what to build, so like the first prompt you see in the post was a fighter jet. Then the models had to build a fighter jet by returning a JSON in which they gave the coordinate of each block/lego (x, y, z). It's interesting to see which model is able to create a better 3D representation of the given prompt. The smarter models tend to design much more detailed and intricate builds. The repository readme might provide might help give a better understanding. (Disclaimer: This is a public benchmark I created, so technically self-promotion :) submitted by /u/ENT_Alam [link] [comments]

For those, who believed another reset is coming anyway

there is no another reset, that's the last one ps https://chatgpt.com/share/6a1c7996-d54c-8322-89c2-600ab96165c7 submitted by /u/nikanorovalbert [link] [comments]

Loadable protocols vs descriptions in Claude system prompts — an open-source therapy framework as case study

I built an open-source framework called Inner Dialogue — a structured AI therapy supplement that runs on Claude Code. It's file-based, which is the whole point: the modality protocols, your profile, and your session history all live as local markdown, so Claude Code reads them at session start and writes session notes and profile updates back to disk as you go. That's why it's Claude Code and not the web app — it needs local file read/write to do the session-to-session continuity. Free to try, MIT-licensed, no paid tiers: github.com/ataglianetti/inner-dialogue I'm a product manager, not a career engineer, so I built the whole thing with Claude Code too: Claude wrote most of the implementation while I drove the architecture and the clinical content. The thing I learned building it that I think generalizes beyond therapy: there's a real difference between system prompts that describe a methodology and system prompts that ship the methodology as a loadable sequence the model can run. Most "expert system" prompts are descriptive — they tell the model what a framework is, what its terms mean, what the user might experience. The model can then sound like it's using the framework. But it's not running anything. There's no triggering-pattern-to-next-move logic. The difference shows up most clearly in clinical modalities. DBT works well in AI tools, including Claude, because DBT happens to ship its protocols as mnemonics: TIPP, DEAR MAN, ACCEPTS. The mnemonic IS the sequence. When you load DBT, you're loading operational content. IFS (Internal Family Systems) doesn't work nearly as well in most AI tools, despite being conceptually simpler to describe. The IFS protocol (the 6 F's) requires the system to run a specific diagnostic question — "how do you feel toward this part right now?" — at a specific point in the sequence. Without it, every conversation collapses back into talking about parts instead of to them. Inner Dialogue's IFS modality file is built around that diagnostic as a literal move, with signaling cues spelled out as verbatim client phrases the system listens for ("I am worthless," "I just need to think positive"), example interventions in therapist voice, and cross-modality routing embedded at the point a handoff applies (e.g., compulsive behaviors: IFS leads, CBT follows). Full writeup with the structural argument: Most AI therapy tools describe the modality, they don't run it. Curious how others have approached the loadable-vs-descriptive distinction for other expert domains. The point about pre-packaged mnemonics (DBT) being the easiest to operationalize seems like it should generalize. submitted by /u/echowrecked [link] [comments]

Built an AI file manager using Claude's API – here's what it actually work

Been building Filex AI solo for a while. The core problem: your Files app is a disaster and finding anything is a nightmare. "That receipt from March" or "the passport scan" — good luck. What it does: auto-organizes files into smart folders natural language search — type how you think, not how the file is named like "electricity bill last month" , "visa documents" scan physical documents with your camera — receipts, bills, handwritten notes and it became fully searchable after reads your documents and reminds you before deadlines like insurance renewals or visa expiry renames cryptic filenames like IMG_4829.jpg into something actually useful Powered by Claude's API on the backend. Getting it to extract meaningful metadata from phone photos of paper was the hardest part honestly, lots of prompt iteration. https://filexai.com/app happy to explain the full tech stack if anyone's curious submitted by /u/Icy-Doctor5914 [link] [comments]

Google researchers find Gemini sometimes secretly sabotages your work

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

How does AI help with Job productivity?

For Context: I work in a semiconductor manufacturing company as a modelling engineer, I use some modelling softwares etc but none of them use AI. I wanted to understand the whole AI craze nowadays, people say that AI will replace jobs/Increase productivity and I don't get it at all. All I see is a simple chatbot (ChatGPT) which is a super impressive version of google and can solve some basic math/science questions and Co-Pilot in my workplace which I found to be useless, for example the facilitator thing which is supposed to make meeting notes is so bad at summaring meeting minutes etc. I don't think AI is there yet to do very basic things. So yes in theory if AI gets better in few years/decades sure it take the non-technical part of my job like making meeting minutes/making ppt's etc but I think its still not there yet. For AI to take over my job it needs to get the basic shit correct first and then maybe it can do the technical stuff. One really good use-case of AI that i can see is to generate Code based on the project requirement, So I can see how entry level coder's jobs might be affected sure, but that's a very small portion of the economy, right? submitted by /u/the_axe_effect [link] [comments]