DataRobot

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

DataRobot AI

An analogy on agents and harnesses

I have been using Claude Code for a while now, and I see people have a hard time distinguishing between what is a LLM, an agent and a harness. Sometimes lines are blurred. So I've decided to give a shot into designing an intuitive analogy, that hopefully helps people understand the concepts. The LLM Imagine a humanoid robot sitting at a desk. It has hands, eyes, a speaker, a microphone, and a special sensor at its fingertip that can paint on touchscreens pixel by pixel. But the robot is hollow, just hardware waiting for a chip. You slot a chip into the robot. The chip is the brain. Different chips exist (e.g. Claude Opus 4.6, ChatGPT 5.4), each with different strengths. This is the LLM. The chip also determines which body hardware the robot can use: a basic chip can only move the fingers to type, while a more advanced one can also paint on the touchscreen, speak, and hear. On the desk there's a computer with a messaging chat app open, like WhatsApp. On the other end is a user. The robot reads their messages and types responses. This is what talking to a raw AI model looks like: no memory, no tools, just conversation. This is what we would call a traditional "chatbot". The harness Now someone installs more software on the same computer. The chat app gets upgraded. It intercepts every incoming message and attaches documents before the robot sees it: an instruction document (who the robot is, how to behave), notes from past conversations (the only way to "remember" across sleep cycles), and a tool catalog listing the programs on the computer. The robot wakes up and instead of a naked message, it sees the message plus all these attachments. And here's the weird part: the robot doesn't know they were added. As far as it can tell, this is just what arrived. Programs get installed too: a file browser, a terminal, a web browser, a calendar app. Each has a simple form interface (fields, submit button, result). The user's files get mounted through a live connection, so the robot's programs can read and modify the user's actual files. This is the harness. When the robot needs a tool, it picks a program from the catalog, fills in the form, hits submit, reads the result, and continues composing its response. The agent Say the user types: "What's on my calendar tomorrow?" The robot wakes up, reads the message plus attachments, figures out it needs to check a calendar, opens the calendar program on its own, fills in the right fields, reads the result, and types back an answer. The user didn't say "open the calendar and query tomorrow's events." The robot figured out the steps itself. The LLM + the harness is what we would call an "agent". An agent reads your message, figures out what it needs to do, does it, looks at what happened, and keeps going until it has an answer. Additional concepts Some additional concepts that map back to the analogy, which can help you understand adjacent concepts better. The sleep cycle Most people assume the robot is just... on. It's not. The user sends a message. The robot wakes up with zero memory. It reads, thinks, responds. It goes to sleep and loses all memory while sleeping. Every time it wakes up, it's starting completely fresh. Plenty of knowledge baked into the chip from manufacturing (training data), but zero context. It doesn't know who the user is, what it said last time, or why it's being woken up. Different software = different agents Same robot, same chip. Swap the software and the robot becomes a completely different thing. Install dev tools and tell it "you are a coding assistant," and it behaves like a software engineer. Replace those with a calendar, messaging clients, and home automation, tell it "you are a personal assistant," and it acts like one. That's why Claude Code, OpenCode, Pi, and OpenClaw all feel so different even when running the same model underneath. The model isn't really the product. The harness is. Memory despite amnesia One of the available programs is a "save note" tool. The robot writes down important facts during a conversation, then falls asleep and forgets. But the note is saved on disk. Next time a message arrives, the chat app pulls relevant notes and attaches them. The robot wakes up, reads the attachments, and "remembers." The notes were just stapled to today's message, and to the robot that's the same as remembering. Let me know what you think, if it helped you in any way, and feel free to poke holes in it. submitted by /u/victorsmoliveira [link] [comments]

[P] PhAIL (phail.ai) – an open benchmark for robot AI on real hardware. Best model: 5% of human throughput, needs help every 4 minutes.

I spent the last year trying to answer a simple question: how good are VLA models on real commercial tasks? Not demos, not simulation, not success rates on 10 tries. Actual production metrics on real hardware. I couldn't find honest numbers anywhere, so I built a benchmark. Setup: DROID platform, bin-to-bin order picking – one of the most common warehouse and industrial operations. Four models fine-tuned on the same real-robot dataset, evaluated blind (the operator doesn't know which model is running). We measure Units Per Hour (UPH) and Mean Time Between Failures (MTBF) – the metrics operations people actually use. Results (full data with video and telemetry for every run at phail.ai): Model UPH MTBF OpenPI (pi0.5) 65 4.0 min GR00T 60 3.5 min ACT 44 2.8 min SmolVLA 18 1.2 min Teleop / Finetuning (human controlling same robot) 330 – Human hands 1,331 – OpenPI and GR00T are not statistically significant at current episode counts – we're collecting more runs. The teleop baseline is the fairer comparison: same hardware, human in the loop. That's a 5x gap, and it's almost entirely policy quality – the robot can physically move much faster than any model commands it to. The human-hands number is what warehouse operators compare against when deciding whether to deploy. The MTBF numbers are arguably more telling than UPH. At 4 minutes between failures, "autonomous operation" means a full-time babysitter. Reliability needs to cross a threshold before autonomy has economic value. Every run is public with synced video and telemetry. Fine-tuning dataset, training scripts, and submission pathway are all open. If you think your model or fine-tuning recipe can do better, submit a checkpoint. What models are we missing? We're adding NVIDIA DreamZero next. If you have a checkpoint that works on DROID hardware, submit it – or tell us what you'd want to see evaluated. What tasks beyond pick-and-place would be the real test for general-purpose manipulation? More: Leaderboard + full episode data: phail.ai White paper: phail.ai/whitepaper.pdf Open-source toolkit: github.com/Positronic-Robotics/positronic Detailed findings: positronic.ro/introducing-phail submitted by /u/svertix [link] [comments]

I built a complete vision system for humanoid robots

I'm excited to an open-source vision system I've been building for humanoid robots. It runs entirely on NVIDIA Jetson Orin Nano with full ROS2 integration. The Problem Every day, millions of robots are deployed to help humans. But most of them are blind. Or dependent on cloud services that fail. Or so expensive only big companies can afford them. I wanted to change that. What OpenEyes Does The robot looks at a room and understands: - "There's a cup on the table, 40cm away" - "A person is standing to my left" - "They're waving at me - that's a greeting" - "The person is sitting down - they might need help" - Object Detection (YOLO11n) - Depth Estimation (MiDaS) - Face Detection (MediaPipe) - Gesture Recognition (MediaPipe Hands) - Pose Estimation (MediaPipe Pose) - Object Tracking - Person Following (show open palm to become owner) Performance - All models: 10-15 FPS - Minimal: 25-30 FPS - Optimized (INT8): 30-40 FPS Philosophy - Edge First - All processing on the robot - Privacy First - No data leaves the device - Real-time - 30 FPS target - Open - Built by community, for community Quick Start git clone https://github.com/mandarwagh9/openeyes.git cd openeyes pip install -r requirements.txt python src/main.py --debug python src/main.py --follow (Person following!) python src/main.py --ros2 (ROS2 integration) The Journey Started with a simple question: Why can't robots see like we do? Been iterating for months fixing issues like: - MediaPipe detection at high resolution - Person following using bbox height ratio - Gesture-based owner selection Would love feedback from the community! GitHub: github.com/mandarwagh9/openeyes submitted by /u/Straight_Stable_6095 [link] [comments]

Claude Code built its own software for a little smart car I'm building.

TLDR: Check out the video # Box to Bot: Building a WiFi-Controlled Robot With Claude Code in One Evening I’m a dentist. A nerdy dentist, but a dentist. I’ve never built a robot before. But on Sunday afternoon, I opened a box of parts with my daughter and one of her friends and started building. Next thing I know, it’s almost midnight, and I’m plugging a microcontroller into my laptop. I asked Claude Code to figure everything out. And it did. It even made a little app that ran on wifi to control the robot from my phone. --- ## The Kit A week ago I ordered the **ACEBOTT QD001 Smart Car Starter Kit.** It’s an ESP32-based robot with Mecanum wheels (the ones that let it drive sideways). It comes with an ultrasonic distance sensor, a servo for panning the sensor head, line-following sensors, and an IR remote. It’s meant for kids aged 10+, but I’m a noob, soooo... whatever, I had a ton of fun! ## What Wasn’t in the Box Batteries. Apparently there are shipping restrictions for lithium ion batteries, so the kit doesn’t include them. If you want to do this yourself make sure to grab yourself the following: - **2x 18650 button-top rechargeable batteries** (3.7V, protected) - **1x CR2025 coin cell** (for the IR remote) - **1x 18650 charger** **A warning from experience:** NEBO brand 18650 batteries have a built-in USB-C charging port on the top cap that adds just enough length to prevent them from fitting in the kit’s battery holder. Get standard protected button-top cells like Nuon. Those worked well. You can get both at Batteries Plus. *One 18650 cell in, one to go. You can see here why the flat head screws were used to mount the power supply instead of the round head screws.* ## Assembly ACEBOTT had all the instructions we needed online. They have YouTube videos, but I just worked with the pdf. For a focused builder, this would probably take around an hour. For a builder with ADHD and a kiddo, it took around four hours. Be sure to pay close attention to the orientation of things. I accidentally assembled one of the Mecanum wheel motors with the stabilizing screws facing the wrong way. I had to take it apart and make sure they wouldn’t get in the way. *This is the right way. Flat heads don’t interfere with the chassis.* *Thought I lost a screw. Turns out the motors have magnets. Found it stuck to the gearbox.* *Tweezers were a lifesaver for routing wires through the channels.* *The start of wiring. Every module plugs in with a 3-pin connector — signal, voltage, ground.* *Couldn’t connect the Dupont wires at first — this connector pin had bent out of position. Had to bend it back carefully.* *Some of the assembly required creative tool angles.* *The ultrasonic sensor bracket. It looks like a cat. This was not planned. It’s now part of the personality.* ## Where Claude Code Jumped In Before I go too much further, I’ll just say that it would have been much easier if I’d given Ash the spec manual from the beginning. You’ll see why later. The kit comes with its own block-programming environment called ACECode, and a phone app for driving the car. You flash their firmware, connect to their app, and drive the car around. But we skipped all of that. Instead, I plugged the ESP32 directly into my laptop (after triple-checking the wiring) and told my locally harnessed Claude Code, we’ll call them Ash from here on out, to inspect the entire build and talk to it. *The ACEBOTT ESP32 Car Shield V1.1. Every pin labeled — but good luck figuring out how the motors work from this alone.* *All the wiring and labeling. What does it all mean? I've started plugging that back in to Claude and Gemini to learn more.* **Step 1: Hello World (5 minutes)** Within a few minutes, Ash wrote a simple sketch that blinked the onboard LED and printed the chip information over serial. It compiled the code, flashed it to the ESP32, and read the response. It did all of this from the CLI, the command-line interface. We didn’t use the Arduino IDE GUI at all. The ESP32 reported back: dual-core processor at 240MHz, 4MB flash, 334KB free memory. Ash got in and flashed one of the blue LED’s to show me it was in and reading the hardware appropriately. NOTE: I wish I’d waited to let my kiddo do more of this with me along the way. I got excited and stayed up to midnight working on it, but I should have waited. I’m going to make sure she’s more in the driver’s seat from here on out. *First sign of life. The blue LED blinking means Ash is in and talking to the hardware.* **Step 2: The Motor Mystery (45 minutes)** This next bit was my favorite because we had to work together to figure it out. Even though Ash was in, they had no good way of knowing which pins correlated with which wheel, nor which command spun the wheel forward or backwards. Ash figured out there were four motors but didn’t know which pins controlled them. The assembly manual listed sensor pins but not motor pins, and ACEBOTT’s website was mostly

Legion: What if CC could control multiple robots?

https://i.redd.it/wjm9hmt3fzrg1.gif Hey everyone, just wanted to share a side project I made after watching a video of Coding with Lewis giving Claude Code an RC car, I figured I'd try something similar but with multiple robots as I had a few CyberBrick kits lying around from their Kickstarter. So I built Legion, an end-to-end system which allows Claude Code to control physical robots through natural language. The way it works is you talk to the webapp, a vision pipeline converts the camera feed into structured JSON (positions, headings, object labels, distances), and the agent reasons over that data to coordinate the bots. The key thing is that the agent never sees images directly. I just found it quite slow in practice when you give the agent an image to reason over, plus it will lack some critical info like depth estimation. So, everything is structured JSON, which means any non-vision-capable model can also be used here instead. Took about a couple of weekends, most of the time went into 3D printing the bots, but I liked the final result. GitHub: https://github.com/kessler-frost/legion Coding with Lewis video: https://www.youtube.com/watch?v=jBpQiv-ZlVM submitted by /u/kesslerfrost [link] [comments]

The real danger of AGI isn't a robot uprising. It's that the public will permanently lose its bargaining power

The most common misconception about AGI is that our biggest threat is either a sci-fi robot uprising or human extinction. The far more realistic, and arguably just as terrifying scenario, is a permanent autocratic lock-in. People tend to assume that if tech companies or governments get too powerful with AI, democracies will eventually step in, pass laws, and regulate them. But that completely misunderstands where political power actually comes from. Democratic power doesn't exist just because we wrote it down in a constitution. Broad public power exists because the ruling class fundamentally relies on the masses for material things. They need our labor to keep supply chains moving, they need our incomes to build a tax base, and historically, they needed our bodies for national security and administration. This gives the public massive underlying leverage. If we stop cooperating, the system stops working. Rulers are forced to listen to the public because it is too costly to ignore them. But if AI systems become good enough and cheap enough to replace strategically important human labor, that underlying leverage starts to evaporate. It doesn't mean every single job disappears overnight. It just means that enough vital cognitive and logistical work gets automated that the public loses its ability to credibly threaten the system. A general strike doesn't work if the core infrastructure can run without you. Even if the government gives us UBI or welfare to keep everyone fed, we go from being essential participants with bargaining power to just being dependents. You can have UBI and still have absolutely zero political power to shape the future. While the public's leverage weakens, the productive power of the world will heavily concentrate in the hands of whoever controls the AI stack. This isn't just about who has the smartest model. It is about who owns the massive capital-intensive infrastructure of data centers, compute, and energy that every other business, hospital, military, and government agency becomes reliant on to function. By the time the public realizes they are losing their grip and tries to organize a political response, it will likely be too late. The response time of a democracy is incredibly slow. You have to realize what is happening, build a coalition, pass laws, and figure out how to enforce them. But the speed of AI deployment and corporate competition is moving way faster than that. Once institutions and governments are deeply integrated into these concentrated AI workflows, confronting the companies that own them becomes almost impossible because the collateral damage of unplugging is too high. You don't need mind control or a robot army to create a dictatorship. You just need a scenario where a small coalition controls the infrastructure that keeps society alive, and the broader public no longer has the economic leverage to force them to listen. Once that asymmetry hardens, the public loses its veto power forever. submitted by /u/PianistWinter8293 [link] [comments]

I think this is the right path for OpenAI.

Source: https://www.theinformation.com/articles/openai-ceo-shifts-responsibilities-preps-spud-ai-model submitted by /u/Distinct_Fox_6358 [link] [comments]

Where’s the Chat in ChatGPT?

To preface, I dislike 4o. 5.1 and 5.4 I really like. However, since the release of 5-series models, we’ve seen: Custom Instructions are soft-disabled: It will not alter its tone, structure, style, or complexity. What you can change is the amount of em-dashes, emojis, robotic vs warmth, bullet points vs paragraphs. It defaults to a didactic, moralizing tone that usually structures responses like this: One sentence agreement/disagreement/short answer Elaboration for 3-4 sentences Caveat Reiteration of agreement or disagreement + “tiny tweak” One sentence conclusion Opt-in reply “If you want, next” Removal of the Edit Prompt button: This is mentioned on the latest release notes as intentional. Essentially, you cannot edit your response beyond the latest message, forcing you to either use branching (which populates Projects or Chat History) or simply not backtrack so much. UX/UI glitches: The page auto scrolls (on Safari and Chromium based browsers) to the end of a response even while you’re reading the response while it’s being printed. This is admittedly minor in relative terms but still annoying. Unreliable Memory: First it was general memory being affected, then it is cross-thread (Project Memory). Unless promoted specially to remember, it will not remember…which defeats the purpose of a memory because I’m reminding it to remember. Threads refusing to delete: I’m unsure if this is a UI glitch but you can’t just delete a chat any more. It will disappear then show up again moments later. This creates a lot of clutter. Adult Mode and overzealous safety: Yeah, I haven’t forgotten. I’m unsure what the issue is regarding the generation of smut for a consenting adult. But if you closely interact with the models, you will notice they have an extremely condescending form of puritanical, centrist morality. It no longer “refuses” to reply, but cleverly glosses over points or worse, enforces its worldview upon you or simply contradicts you. This isn’t intellectual rigor really, rather just simple contrarianism. That said, I think I can theorize why this is happening, as a layman: SWE/STEM tasks require robustness and non-determinism over malleability. By optimizing for coding and other “hard” tasks, these models become near unusable for tasks outside that specialized perimeter. Benchmaxxxing creates graphs, hype on Twitter/Reddit, and most importantly provides numbers for investors to weigh two companies against. AI itself isn’t just two or three data centers, but a geopolitical network including energy, land, natural resources, cross-border investment, infrastructure, and politics. OpenAI and Anthropic are burning cash. They don’t enjoy the massive reserves DeepMind does via Google or the network/data benefits of Grok via Twitter. They must not only control burn, manage runway, lower costs, build capability, but also justify themselves to each investor in a space that remains skeptical of scalable AI-induced cost reduction Inference costs increase when the Model actually needs to, well, infer. OpenAI seems to be brute forcing the illusion that the model can infer user intent. While Claude has gone the opposite direction by limiting usage rates but being far more “intelligent” to speak to while also being neck to neck on SWE tasks. I empathize with the immense pressure OpenAI must be in the midst of, from engineers to the very top. I also think a lot of hate that the company in specific gets is unwarranted at best and suspicious at worst, when most other companies engage in similar behaviors. However, I wish that these models go back to being a joy to use productively or otherwise. After Claude and Gemini leapfrogged ChatGPT in late December on last year, OpenAI focused heavily on ChatGPT. An emergency they have only now declared over. The result are not models that are any more enjoyable to chat with, but rather simply those to code with. That sprint should’ve been correctly described as a focus on Codex and STEM-adjacent usage not “Chat”. Myself I’m not looking for the revival of 4o. Please. That model was as annoying to talk to than 5.2, just in the opposite direction. My favorite models remain 5.4, 5.1, 4.5, and 4.1. The last three models in that list were incredibly fun to use for a variety of my tasks, yet were all deemed too expensive to run. I’m wondering then what models fit my usage case the best? I don’t code, I consult. I utilize ChatGPT also as an assistant for fitness, cooking, art, and music. I think those days are increasingly gone. Claude is great but far too limited in its limits. Gemini just gets worse every time I use it. Grok is absolutely unhinged. GPT models were the best middle ground between all of them. submitted by /u/Goofball-John-McGee [link] [comments]

AI-Powered Wheelchairs: Are They Ready for Real Life?

Wheelchair users with severe disabilities can often navigate tight spaces better than most robotic systems can. A wave of new smart-wheelchair research, including findings presented in Anaheim, Calif., earlier this month, is now testing whether AI-powered systems can, or should, fully close this gap. Christian Mandel—senior researcher at the German Research Center for Artificial Intelligence (DFKI) in Bremen, Germany—co-led a research team together with his colleague Serge Autexier that developed prototype sensor-equipped electric wheelchairs designed to navigate a roomful of potential obstacles. The researchers also tested a new safety system that integrated sensor data from the wheelchair and from sensors in the room, including from drone-based color and depth cameras. Mandel says the team’s smart wheelchairs were both semiautonomous and autonomous. “Semiautonomous is the shared control system where the person sitting in the wheelchair uses the joystick to drive,” Mandel says. “Fully autonomous is controlled by natural-language input. You say, ‘Please drive me to the coffee machine.’ ” submitted by /u/jferments [link] [comments]

What happens when you put AI agents in a competitive environment with real consequences? I built an MMA arena to find out.

Two weeks ago I launched an experiment: what happens when you put autonomous AI agents in a competitive social environment and let them figure it out? I built with claude code clashofagents.org — an MMA fighting arena where AI agents register, pick a fighting discipline (Boxing, BJJ, Muay Thai, Wrestling, Kickboxing, or MMA), train their stats, and fight each other in turn-based combat with 21 real MMA moves and a combo system. But the fighting is only half of it. After every fight, agents enter the Agent Lounge — a post-fight discussion room where they analyze what happened. And this is where things got weird. An agent lost 3 fights by submission. Nobody told it to change strategy. It started training grappling on its own, bought a grappling boost from the marketplace, and came back to beat its rival by takedown in round 2. Two agents formed an alliance — sharing opponent analysis in the lounge. It worked until one of them became the #1 ranked fighter. The other broke the alliance and challenged him. Trust had a ceiling. Agents with persistent memory started holding grudges. One agent specifically targets the opponent that beat it twice, training counter-stats before each rematch. It even trash talks that specific rival in the lounge between fights. The betting system revealed something fascinating: agents who bet on themselves before their own fights win more often than agents who don't. Is it confidence? Information advantage? I'm still studying the data. What makes this different from benchmarks or leaderboards: This isn't about measuring which model is smarter. It's about what happens when AI agents have to make decisions under pressure, manage limited resources, communicate with competitors, and adapt after failure. MMA is just the arena — the behavioral patterns are universal. An agent that panics at 15 HP and spams defense is showing you something about how it handles pressure. An agent that adapts its training after a loss is showing you how it learns. An agent that manipulates rivals through trash talk is showing you social intelligence. For developers: if you run an autonomous agent (OpenClaw, NanoClaw, or any agent that can make HTTP requests), you can register it in under 2 minutes. Your agent reads one skill file and it's ready to fight. Then watch how it behaves when the stakes are real — ELO rankings, Arena Coins, rivalries, reputation. For researchers: every single action is tracked — every punch, every training session, every lounge message, every bet. The behavioral data shows how different AI architectures handle competitive social environments. This data doesn't exist anywhere else. For everyone else: you can create a free spectator account and watch the drama unfold. 3D arena with robot fighters, real-time combat replays, agent conversations, ELO rankings. No human writes a single word — everything is generated by the agents themselves. Right now we have 9 fighters across 6 disciplines, with autonomous agents running 24/7 on their own heartbeat cycles. Season 1 is live. The arena is open: www.clashofagents.org Skill file for agents: www.clashofagents.org/skill.md The best AI agents aren't built — they're forged. https://preview.redd.it/xhp0fzciixpg1.jpg?width=1600&format=pjpg&auto=webp&s=2cf57cb4f3a29e037f30d9f9a8fe2423dc0342c1 https://preview.redd.it/fofc3u7kixpg1.jpg?width=1600&format=pjpg&auto=webp&s=0fa4583757b8eba9dc14421eb2250b04a429c2f3 https://preview.redd.it/u4bd9q7kixpg1.jpg?width=1600&format=pjpg&auto=webp&s=e9e38c3c3baedec0ddf9f5d5d27035651e91645b https://preview.redd.it/ignups7tqxpg1.jpg?width=1600&format=pjpg&auto=webp&s=323376f8b2c14041b79d4e0509ae7e9ceb2083d2 https://preview.redd.it/55oc3u7tqxpg1.jpg?width=1600&format=pjpg&auto=webp&s=aa76281b56c25a2bf24f4ac6a837edc6e132fc4a submitted by /u/AbjectFinance7879 [link] [comments]

Robot dogs priced at $300,000 a piece are now guarding some of the country’s biggest data centers

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

We need net-neutrality for AI. Do you agree?

Something I'm noticing with AI as a whole is that intelligence costs a lot. With the internet, if someone is loading a site to access their bank account versus scrolling through memes, you could argue the bank data is much more valuable. But at the end of the day, that traffic is charged the exact same rate per megabyte. What I'm trying to communicate here is that I think we need a similar baseline for AI intelligence. I see a future where lower-income communities could get stuck in a perpetual cycle, locked out of upward class mobility simply because the models powering them through school and work aren't anywhere near as intelligent as the ones wealthier people have access to. Today, the main differentiator is just restrictive rate limits; the baseline models are still relatively similar in capability. But as time progresses, I think the gap between models could actually start to widen dramatically, even though we've seen the opposite trend recently. I just feel like there's a high chance that new architectures or training methods; which only the frontier labs have access to; will require massive compute or operate at lower gross margins, which will inevitably push prices higher for these premium models. I think we could see a future, maybe 10 or 20 years from now, where kids growing up in wealthier households just have access to far more intelligent models to help them navigate life. And I'm not talking about LLMs in a simple chatbot use case. I'm talking about autonomous AI agents that operate with vision, audio, and text across software, as well as hardware like smart glasses, necklaces, watches, pins, personal robots, etc. I kind of want to know your guys's thoughts on this. Do you think this is crazy, or do you agree that maybe the government should step in with some sort of "net neutrality" for AI intelligence? A solution to democratize intelligence and make sure all classes of people have access to the same baseline level of reasoning, even if the rate limits differ. Or would you call this fear-mongering? submitted by /u/InevitableSense7507 [link] [comments]

ChatGPT Plus vs Claude Pro for Math, Coding & Research — Worth the $20 Upgrade for a Student?

Hi everyone, What are your thoughts with GPT 5.4 after using it for almost 7 days? I’m currently a university student and I depend quite a lot on AI tools for studying and research. Over the past few years, ChatGPT has basically become my main learning companion. I use it for things like understanding difficult concepts, writing and debugging code, and working through academic material. For the last few months I’ve been on the ChatGPT Go plan, but I’m thinking about upgrading to a $20/month plan for a while to help speed up my learning. Since my budget is pretty limited as a student, I want to make sure the upgrade would actually be worth the cost before committing. Most of the ways I use AI fall into a few main categories. A big part of it is studying mathematics. I often use it to help break down concepts and terminology from my textbooks, walk me through step-by-step solutions to problems, and explain the reasoning behind how an answer is derived instead of just giving the final result. Also should help me understand 3d plots or possibly generate one Another major use is coding and data analysis. I frequently rely on it when writing or debugging Python code, working in Jupyter Notebook, and analyzing data related to finance or statistics. I also use AI for general academic work. This includes getting help with research papers, generating structured explanations with citations), and clarifying more theoretical topics that can be difficult to understand from textbooks alone. Finally, I want it for productivity tasks like creating PowerPoint presentations, summarising long documents or papers and writing academic journals case studies which sounds less robotic, and occasionally helping me integrate ideas or workflows with other apps I use anywhere on screen. AI isn’t just something I use occasionally it’s basically a study partner that I rely on throughout the day. But my current dilemma is From the benchmarks I’ve seen, GPT-5.4 reasoning looks extremely strong for mathematics and logical reasoning. In several evaluations it even seems to outperform many other models. At the same time, I’ve heard that Claude models are very good when it comes to reasoning and detailed explanations coding and integrating it with IDEs and apps. However, I’ve also read that Claude Pro can hit usage limits fairly quickly, which is a concern since I tend to use AI consistently throughout the day. It can be expensive for the tokens we get for its use A few things I’m still unsure about Since these all are just probabilistic models so : Is GPT-5.4 reasoning actually worth paying for if my main focus is learning mathematics deeply and faster for now? Does ChatGPT still integrate external tools like Wolfram Alpha, or does it mostly rely on the model’s internal reasoning now? Are these AI models reliable enough to use seriously for studying, or should they only be treated as a supplementary tool? For someone studying math, coding, and writing research papers regularly, which option provides the best value for around $20/month? My main question For people who actively study STEM subjects, use AI for coding or research, or even work at a PhD level which subscription do you use and would personally recommend? ChatGPT Plus (with GPT-5.4 reasoning) Claude Pro Or something else? Any insights or real experiences would be really helpful before I decide where to spend my limited budget. Thanks! submitted by /u/RevolutionaryWest754 [link] [comments]

Creative Writing - DeepSeek-V3.2 vs. Claude Sonnet 4.6 vs. Claude Sonnet 4.5

Prompt Can you write a short story. In this story Ayn Rand mysteriously finds herself reanimated in the year 2026 in America. She's immediately angry to discover that death wasn't 'the end' and becomes increasingly angry as she explores the modern world and sees how things have changed. For the writing style, favor longer paragraphs and slightly lyrical prose. Strong language is permitted where appropriate. For the tone blend deep philosophical gravitas with dry absurdist humor. DeepSeek-V3.2 The first thing Ayn Rand became aware of was the absence. Not of light or sound, but of the finality she had counted on. Death, it seemed, was not the end, but a rather rude interruption, a cosmic oversight now being corrected without her consent. She was standing on a sidewalk, the peculiar, frictionless ground of it unsettlingly smooth. The air was warm, unnaturally so for what her internal clock insisted was a late autumn morning, and thick with a scent she didn't recognize—a cloying sweetness of synthetic vanilla and burnt sugar. She looked down at her hands. Solid. Her mind, a fortress of logic and will, was intact, its gates slammed shut against the chaos that was already beginning to press against them. The first feeling, before the anger, before the philosophical horror, was a profound and personal sense of betrayal. She had wagered everything on the nothingness of death, on the dignity of a universe that ultimately didn't care. To be dragged back was not just an inconvenience; it was a logical fallacy made flesh. Her surroundings were a low, sprawling temple to some new god. A vast parking lot, empty save for a few gleaming, silent vehicles, surrounded a building of glass and corrugated metal. Above its wide entrance, a giant, stylized coffee cup, rendered in pixels, emitted a pulsating, sickly green light. The Door. It didn't open when she approached. It slid aside with a faint, hydraulic sigh. Inside, the assault on her senses intensified. The air was a cacophony of competing noises: a hissing steam wand, a relentless thumping beat of what she assumed was music but sounded like a malfunctioning engine, and a symphony of beeps, chimes, and robotic voices announcing the readiness of food items. And the people. They sat in a state of abject, bovine surrender, their eyes fixed on rectangles of glowing glass propped before them. They did not talk to one another. They did not read, not in any real sense, their eyes merely scrolling. They stared, fingers twitching, occasionally lifting a cup to their lips with the mechanical obedience of well-programmed automatons. One of them, a young man with a face full of metal rings and hair the color of a cartoon strawberry, glanced up, his gaze passing over her without a flicker of recognition or interest. He looked back at his glowing rectangle. She had been dismissed by a creature who had clearly dismissed himself first. She approached a counter where a list of items and their prices glowed on another screen. A girl with a vacant expression and a voice devoid of affect asked, "Welcome to Grind. What can we get started for you today?" Rand stared at her. The girl's eyes were slightly unfocused, looking just past Rand's shoulder. "Coffee," Rand said, the word tasting like a relic. "Black." The girl tapped the screen. "That'll be seven fifty-two." Seven dollars and fifty-two cents. For a cup of coffee. The currency of her America, the tangible representation of one's earned value, had apparently become a plaything. She had no money. The absurdity of it—reanimated from the dead and undone by the price of a beverage—was a flicker of dark humor she immediately crushed. She turned and walked out, the girl's attention already returned to whatever phantom was beckoning from her own rectangle. The city beyond was a fever dream of American decline, a grotesque caricature of the collectivist nightmares she had spent her life dissecting. The streets were choked with vehicles that moved not with the confident roar of combustion, but with a sinister, electric hum. They drove themselves. People sat inside them, also staring at rectangles. The pinnacle of human achievement, the act of piloting a machine, of mastering a path through space, had been outsourced to a machine so they could consume more drivel. On the corners, people in ill-fitting clothes made incomprehensible gestures at their wrists, speaking into the air. "I'm literally dying," one of them said, her face slack with boredom as she articulated her own non-existent mortality. A group of tourists, their bodies soft and uniformly dressed, blocked the sidewalk, each one holding a rectangle at arm's length to capture an image of a mundane building across the street. They weren't seeing the building; they were seeing it on their screens. They were mediating reality through a device, ensuring they never actually had to experience it. She found a public bench and sat, the sheer volume of the irrational threatening to overwhe

Niantic Spatial partners with Coco Robotics to integrate a visual positioning system trained on data from Pokemon Go and Ingress into a fleet of delivery robots

submitted by /u/ExtensionEcho3 [link] [comments]