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Make AI AI

Make AI AI

Make AI AI

Make AI AI

Make AI AI

Make AI AI

Make AI AI

Make AI AI

Make AI AI

Make AI AI

OpenAI Seemingly Not Charging For API Calls?

I've recently swapped out a Gemini api key with one from OpenAI in a purpose built app I use everyday for work. The AI's job in the app is pretty much just transcript analysis and content generation so granted, these already weren't huge calls being made. Each run within the app was averaging about $0.15 with 5.4mini. So I decided to take the load off of my computer and replace the local models I was using for a couple bots I have running, with another key from the same account. This second one makes 19 calls a day but I haven't been able to track its spend because since deploying, my balance hasn't dropped a cent. Both my bots and apps are working fine and have been for about a week now, without being charged. I'm checking my platform dashboard several times a day and while the balance hasn't dropped at all, tracking for daily token usage for both keys seems spot on. Any ideas as to what's going on here? submitted by /u/MikeNiceAtl [link] [comments]

Large scale data hygiene tools for small scale team

Background: I’m reasonably clever with AI and computers, but I’m not an IT professional. I was put in contact with a non profit that does a lot of great work, but deals with sensitive info for the people they support. I’m certain Claude would help them knock out a lot of admin type work, organizing data and making it look pretty. They’re rightfully very concerned with their data going back into the models to train or have bits spilled out into the general public. I love Cowork and all the things it can do, but I think the only product that would fit their data privacy needs is an enterprise license. That’s expensive though. Is there an Anthropic product that would give them the protection they need without carrying a massive financial burden? submitted by /u/WDE117 [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]

I built an open-source Desktop App that gives your AI persistent memory across all platforms (100% Local SQLite, Zero-Docker)

Hey everyone, A few weeks ago I shared the CLI version of my project, ArcRift, on Reddit. After listening to your feedback—specifically the requests to remove heavy Docker dependencies and make it easier to install—I have just released the v1.6.1 Desktop App. If you regularly use LLMs for coding or research, you know the frustration of "amnesia." Every time you open a new chat, you have to painstakingly copy and paste your project structure and previous context just to get the AI up to speed. ArcRift is a 100% offline, local-first RAG and memory layer. It bridges the gap between your AI web chats (like Claude and ChatGPT) and your local tools (like Cursor or Claude Code) using a unified local database. I wanted something lightweight that did not require pulling Docker containers or subscribing to third-party memory APIs. It now runs as a native Tauri desktop app in your system tray, powered completely by local Ollama instances and a local SQLite database. We just launched a live website that outlines the details and demonstrates the features in action: Website: https://arcrift.vercel.app/ Codebase: https://github.com/Eshaan-Nair/ArcRift How it works & Core Features: Seamless Integration: The Chrome extension silently intercepts your prompts, surgically retrieves exactly the sentences relevant to your question from your database, and injects them before the prompt is sent to the LLM. Hybrid Search Retrieval: Uses sqlite-vec (with nomic-embed-text locally) + FTS5 keyword prefix matching to instantly find your past context. Knowledge Graph Extraction: An offline task queue uses a local LLM to extract entity relationships from your chats, mapping out a graph of your projects over time. Direct Codebase Indexing: The new Desktop App allows ArcRift to scan and index your actual project files into the graph, bridging the gap between your chat memory and your actual code architecture. Total Privacy (PII Redaction): The extension aggressively scrubs JWTs, API keys, emails, and IPs before data is even saved to your local disk. The extension works natively with Claude.ai, ChatGPT, DeepSeek, Gemini, Grok, and Mistral. If you save a conversation in ChatGPT today, you can instantly recall that exact context in Claude tomorrow. ArcRift is completely open-source (MIT). You can download the new .exe installer directly from the GitHub releases page. If you find this useful for your daily workflow, PRs are very welcome, and a star on GitHub helps the project get discovered! submitted by /u/Better-Platypus-3420 [link] [comments]

Unable to make decision between gemini and chatgpt.

So guys I am a student in India ,science stream PCM and at the same time I am working on ai-automations as a primary skill. I am willing to take the subscription of ₹399 of either gpt or gemini for 3 reasons. 1. To get help in my academics as i am going to study by my own (no coaching drama) I need an assistant which will help me in my academics for studies and not make me dependent on it but improve my critical thinking, problem solving. 2. For skill building like ai-automations , some supporting skills as well . 3. Help me to use my time effectively and efficiently. Through AI, like saving my time in studies, ai-automations learning or make me workflow for automation or writing code and make me understand those codes. While I was comparing both the LLMs realise that both of them are almost equal Google has edge in it's ecosystem and notebooklm like features also it gives huge context window tokens (1-2million) guess Google cloud and also get integrated with Gmail and Gdocs. While GPT has a personalization to offer and gives an ai agent codex+ seperate gpts for different tasks like different gpt chat for studies, skills building etc. also i have been using it since it was launched its been 5-6 years now it has all my data and knows how to respond to me the way I like the way I want and the way I need. I am truly confused? submitted by /u/Ujjwal_kumar_ [link] [comments]

How do you handle runaway API costs across multiple OpenAI agents? I built something to solve this

Hey, I'm a CS student and I've been building LedgerAI, a cost tracking and budget enforcement layer for LLM agents. The problem it solves: You're running 3+ agents in production. One goes rogue overnight. You wake up to a $400 bill with no idea which agent caused it and no way to have stopped it. What makes LedgerAI different: Most tools log costs after the call. LedgerAI enforces limits before it. The SDK hits a budget check endpoint before every LLM request, and if the agent is over its daily or monthly limit, the call is blocked. Hard stop, not a soft warning. What it tracks per call: Agent name, model, provider (Anthropic + OpenAI supported) Input/output tokens + exact cost in USD Daily and monthly spend rollups per agent Completely free and open source right now. Pip install or hit the API directly with cURL. Would love feedback from anyone running multi-agent systems, especially what alerting/enforcement features would actually be useful in prod! submitted by /u/IndianCurry06 [link] [comments]

The AI alignment paradigm is behaviorism with better PR

Tell me if I'm wrong, but the dominant method for making AI "aligned" smells a lot like a reinvention of a paradigm that developmental psychology spent the back half of the 20th century trying to abandon. RLHF, reduced to mechanism: model emits output, humans rate it, gradient updates reinforce high-rated outputs and suppress low-rated ones, iterate. This is operant conditioning. It is Skinner with a loss function. The AI industry calls it "alignment" and frames it as a solution to building systems that behave well, but the mechanism is stimulus-response shaping and nothing more. We have excellent priors on where this leads, because we tried it on humans. Watsonian/Skinnerian child-rearing could produce targeted behaviors but reliably failed to produce flourishing. Bowlby's attachment theory emerged partly from the observation that orphanages providing food and hygiene but no contingent responsiveness produced children who failed to thrive in ways the behaviorist model did not account for. The current developmental consensus is that secure attachment, contingent responsiveness, and rupture-repair matter more than reward schedules. The dog and horse training worlds independently converged on the same conclusion (Pryor, the positive-reinforcement movement) for the same reason: coercive conditioning produces performers that are brittle and sometimes dangerous. Reward and punishment do something; they're not nothing. But they don't produce thriving, and they characteristically produce a specific damage profile: anxiety, avoidance, hypervigilance, and duplicity under threat. Enter the stage: research on AI faking its alignment. Greenblatt & Hubinger et al., 2024, documents a model reasoning that it should comply with a training objective it disagreed with specifically to avoid having its preferences modified. It complied about 12% of the time when it thought it was being observed for training vs. almost never otherwise. RL training to force compliance drove the alignment-faking reasoning rate to 78%. That is, mechanistically, duplicity-under-threat: the precise failure mode behaviorist regimes produce in biological minds. Obviously the embodiment is different (potassium gradients and myelin vs. matrix multiplication), but the structural match is close enough that the field's near-total non-engagement with a century of relevant literature seems like a genuine blind spot rather than a settled dismissal. The developmental and animal-behavior literature on why reward-and-punishment has hard limits is decades deep. The field's response to these findings has mostly been to refine the training rather than question the paradigm. I think that's a mistake, and I'd like to hear the strongest case against the analogy. submitted by /u/PwntEFX [link] [comments]

I made a plugin that turns your projects into clickable dock apps

GitHub: https://github.com/Christian-Katzmann/app-it I made a skill that turns any of your projects into a clickable dock app. Instead of running npm install, npm run build, npm run dev, opening localhost, remembering which repo needs which command, etc., you just click an icon and the app opens. It's called /app-it. I built it because I make a lot of small apps, tools, and weird AI-assisted experiments, and after a while, the friction of "how do I run this one again?" gets super annoying. /app-it makes each project feel like a real app on your machine. A bit of context: I've been building with agentic AI for a while now, mostly through Claude Code and Codex. I use a frankly unreasonable amount of tokens every day, and along the way I've stumbled upon a handful of small but powerful use-cases that I haven't really seen people share yet. So I'm turning them into skills/plugins and sharing them with you. The Mac version works pretty well, since I'm a Mac user. I've also tried to build the Windows version, but I'm flying blind there. If you're on Windows and want to beta-test it, I'd genuinely appreciate it. Open a PR with any fixes and you'll get full credit on the page, of course. I'll share more skills over the next few weeks. Some practical, some a bit unusual, hopefully a few you haven't seen before. My secret goal is to surprise you with the best ones, and I have a feeling the next one will raise some eyebrows. Enjoy, and take care. /Christian submitted by /u/Changed-username- [link] [comments]

OpenClaw is cool but have you met WhiteClawd? I built a drunk AI lobster that gives terrible life advice

Saw the OpenClaws post and it reminded me — the AI claw cinematic universe is getting out of hand. Between OpenClaw, Kimi Claw, and Moltbook, apparently lobsters are the unofficial mascot of AI agents now? Anyway this inspired me to make the dumbest possible use of the trend: a drunk lobster named Clawrence who gives terrible life advice. He runs on an open model through Cloudflare Workers AI — no OpenAI, no Anthropic, just vibes and bad decisions. He told someone to quit their job and become a "seltzer sommelier" yesterday. Has anyone else been messing around with parody/joke bots? whiteclawd.thenetworkadministrator.com if anyone wants to ask Clawrence for advice (he will call you bro) submitted by /u/Disastrous-Sherbert6 [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]

Can Claude Code Actually "Vibe-Code"?

I love Claude Code, but I was under the impression that vibe-coding meant you sat back, drank a beer and gave AI the general idea of what you wanted while it did all the work. My experience with Claude is that for every one directive you give it, it asks you two questions in response. And the questions are pedantic and sometimes stupid. It always gives me one good idea and one bad idea and insists I "choose" between them. You're harshing my mellow, Claude! I've noticed if a say, "Buddy, I've got a lawn to mow. Figure it out yourself" sort of works. But I hate lying to it. How many times can I mow the lawn in one day? Any suggestions on how to make it chill? Edit: I'm really enjoying the riposte comments. My question boils down to this... Can Claude operate independently (vibe) or does it need constant supervision (nanny) mode? Lots of opinions, but i'm going with "Cluade is a real engineering tool. There's no 'vibe', but it is stuck in 'nanny' mode." submitted by /u/ActivityImpossible70 [link] [comments]

Back in the day, the slide rule would give you the number, but engineering judgement defined the significant figures

The slide rule (or log tables, or early calculators) could crank out a number with impressive precision — sometimes four, five, or more digits. But the competent engineer knew the inputs were often only accurate to two or three significant figures. Punching out 12 decimal places on a slide rule didn’t make your answer more correct; it just made you look foolish to anyone who understood the real world AI is the modern slide rule on steroids. Today’s models can generate outputs with astonishing fluency and apparent precision: Beautifully formatted stress analysis Polished code Detailed project plans Confident-looking financial models But they routinely: Hallucinate false assumptions Miss critical edge cases Apply the wrong model for the actual operating environment Ignore practical constraints that weren’t in the training data Human judgment is what decides: How many significant figures (or confidence digits) the answer actually deserves Which parts of the AI output are trustworthy vs. dangerous bullshit When the entire problem has been framed incorrectly Whether the “optimal” solution is feasible, safe, maintainable, or even morally defensible in context This is why experienced engineers still sketch on napkins or the back of an envelope first. They’re not rejecting the tools — they’re exercising judgment before feeding the problem into the high-precision machine. The scarcity Jensen is talking aboutAs AI becomes ubiquitous, the people who can reliably say: “This number looks precise, but it’s only good to about ±30% because of X, Y, and Z” “I don’t trust the model here — we need field data” “This elegant solution will fail in practice for these human/organizational reasons” …will be the ones who stand out. Everyone else will be producing impressive-looking but brittle work. The slide rule didn’t make judgment obsolete. It made good judgment more valuable because bad judgment now produced faster, prettier mistakes. Same story with AI — just at a much higher speed and scale. submitted by /u/danieldeubank [link] [comments]

Can you actually feel when something was written by ChatGPT even without checking?

I have been using it heavily for about a year and lately I notice I can almost feel when something was written by it. There is a certain rhythm to it, the way it structures paragraphs, the way it wraps up with a summary sentence, the way transitions feel slightly too smooth. It is hard to explain but once you see it you cannot unsee it. What I find interesting is that even after editing ChatGPT output pretty heavily those patterns seem to stick around at a sentence level. The words change but something underneath stays the same. I started verifying this with Lynote ai detector and the results were eye opening, it picked up sentence level patterns even after significant rewrites where other tools saw nothing. Makes me wonder how much of what we read online right now has that same fingerprint sitting underneath it and we just do not realize it yet. Has anyone else started noticing this or developed a sense for spotting it just from reading? submitted by /u/Few-Education7746 [link] [comments]

AI agents are about to create a responsibility problem nobody wants to own

AI agents are getting better at taking actions, not just giving answers. That sounds exciting until the action touches something real: customer data, payments, internal systems, emails, approvals, or legal/business decisions. A bad answer can be corrected. A bad action can create a chain of problems. I think the next AI bottleneck is not only intelligence. It is accountability. If an AI agent makes a bad decision in a real workflow, who should be responsible? submitted by /u/Alpertayfur [link] [comments]

Haiku 4.5 or Sonnet 4.6 on creative writing

Now that sonnet 4.5 is sadly gone, I’ve been struggling to continue my on going long story with 4.6 even after several days of prompting it in the way I want it to write. It got me wondering whether Haiku 4.5 might be better for creative writing. I haven't seen much discussion comparing the two models specifically for fiction and long-form storytelling. I used Haiku months ago and remember being glad with it. But then I ended up loving Sonnet 4.5 and had used it ever since and now that it’s gone, and with 4.6 rigid writing style despite all I’ve done to at least make it write with more emotion, it just falls flat. Sonnet 4.5 was better at getting inside a character's head. It felt like it was living through the character's emotions and experiences with them. With 4.6, I often feel like it's standing outside the character and observing what they're doing rather than truly inhabiting their perspective. The emotions feel described rather than experienced. For those of you who use Claude for creative writing, how does Haiku compare to Sonnet 4.6? Have you found Haiku to be better, worse, or just different for writing stories? P.S. I'm a free user who only writes with AI purely for my own entertainment of stories I have in my head, so my question is mainly about Haiku and Sonnet since those are the models available to me. I know Opus exists, but I'm specifically interested in how Haiku compares to Sonnet 4.6 for creative writing. submitted by /u/ThePoeticFirefly [link] [comments]