SearchGPT is praised for its unique capability of improving automated hyperparameter search by leveraging access to research literature, leading to enhanced results in experiments. However, there are no significant direct positive or negative reviews about it elsewhere, indicating limited user engagement or feedback. The pricing sentiment is unclear due to lack of explicit mentions, but there is generally no significant complaint about cost within the covered mentions. Overall, SearchGPT seems to be recognized within specific technical communities, but lacks a broader reputation or widespread user feedback.
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SearchGPT is praised for its unique capability of improving automated hyperparameter search by leveraging access to research literature, leading to enhanced results in experiments. However, there are no significant direct positive or negative reviews about it elsewhere, indicating limited user engagement or feedback. The pricing sentiment is unclear due to lack of explicit mentions, but there is generally no significant complaint about cost within the covered mentions. Overall, SearchGPT seems to be recognized within specific technical communities, but lacks a broader reputation or widespread user feedback.
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Someone used AI to explain a Dune passage warning against using AI to do your thinking. That's the whole debate
The Globe and Mail's editorial board ran a piece in March titled "AI can be a crutch, or a springboard." To illustrate the crutch half, they offered this: someone asked AI to explain a passage from Dune that warns against delegating thinking to machines. Instead of reading the book. That anecdote is doing more work than the studies the editorial cites. But the studies are real. Researchers at MIT published a paper in June 2025 titled "Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task" (Kosmyna et al., arXiv 2506.08872). The study tracked brain activity across three groups: people writing with ChatGPT, people using search engines, and people working unaided. The LLM group showed the weakest neural connectivity. Over four months, "LLM users consistently underperformed at neural, linguistic, and behavioral levels." The most striking finding: LLM users struggled to accurately quote their own work. They couldn't recall what they had just written. The Globe cites this and similar research to make a point about dependency. The implicit argument: hand enough of your thinking to a machine and you stop doing it yourself. That finding is probably accurate for the way most people use these tools. The question is whether that's the only way they can be used. The Globe's own title contains the counter-argument. Crutch or springboard. They wrote both words. They just didn't develop the second one. Ethan Mollick, a professor at Wharton who has been writing about AI use since the tools became widely available, argued in 2023 that the real challenge AI poses to education isn't that students will stop thinking, it's that the old structures assumed thinking was hard enough to enforce. ("The Homework Apocalypse," [oneusefulthing.org](http://oneusefulthing.org), July 2023.) When AI can do the surface-level cognitive work, the only tasks left worth assigning are the ones that require actual judgment. The tool, in that framing, doesn't reduce the demand for thinking. It raises the floor under it. Nate B. Jones, who writes and consults on what it actually takes to work well with AI, has made a sharper version of this argument. His position: using AI effectively requires more cognitive skill, not less. Specifically, it requires the ability to translate ambiguous intent into a precise, edge-case-aware specification that an AI can execute correctly. It requires detecting errors in output that is fluent and confident-sounding but wrong. It requires recognizing when an AI has drifted from your intent, or is confirming a premise it should be challenging. These are not passive skills. They are harder versions of the same thinking the MIT study found LLM users weren't doing. The difference between the group that lost neural connectivity and the group that doesn't isn't the tool. It's what they decided to do with it. Here's my own evidence. In the past year I built a working web application. Python backend. JavaScript frontend. Deployed on two hosting platforms. Payment processing. User authentication. A full data model. I do not know how to code. Every product decision was mine. Every architectural call. Every tradeoff judgment. I defined what the system needed to do, why, and what done looked like. I reviewed every significant change before it was accepted. When something broke, I identified where the breakdown was and directed the fix. The implementation was handled by AI. The thinking was mine. This mode (call it AI-directed building) is the opposite of the Dune reader. The quality of what gets produced is entirely a function of how clearly you can think, how precisely you can specify, and how critically you can evaluate what comes back. There is no shortcut in that. A vague brief to an AI doesn't produce a confused output. It produces a confident, fluent, wrong one. The discipline that prevents that is yours to supply. Non-coders building functional software with AI is common enough now that it isn't a story. What's less visible is the specificity of judgment underneath the ones that actually work. The practices that force more thinking rather than less are not complicated, but they require a decision to use the tool differently. When I've formed a position on something, I give the AI full context and ask it to make the strongest possible case against me. Ask for the hardest opposing argument it can construct. Then I read it. Sometimes it changes nothing. Sometimes it surfaces something I had dismissed without fully examining. The AI doesn't form my view. It stress-tests one I've already formed. When I'm uncertain between options, I don't ask which is better. I ask: here are two approaches, here is my constraint, now what does each cost me, and what does each require me to give up? I make the call. The AI laid out the shape of the decision. The judgment was mine. The uncomfortable part of thinking is still yours in this mode. The tool makes the work more rigorous,
View originalWhy self-reflection ReAct loops fail on long-horizon tasks, and the AgentOS verification architecture we built to fix it.
Saw a great discussion earlier in this sub about the limits of self-reflection and whether a separate verifier agent is actually worth the compute overhead. It highlighted a huge flaw: Having an agent grade its own scratchpad almost guarantees rubber-stamping: it reflects on its work with the exact same blind spots that produced the error. Here's the architecture we built for the Apodex-1.0 Heavy-Duty Solver to get verification out of the reasoner's head entirely. The dominant approach right now is the ReAct paradigm—one agent in a think-act-observe loop inside a single context window. Empirically, these loops hit a hard ceiling after a few hundred steps: the context congests, parallel branches of inquiry contaminate one another, and self-reflection degrades. An agent reflecting on its own work has the same blind spots that caused the error in the first place. We call this "pseudo-correctness"—an answer that looks confident, passes basic checks, but is structurally flawed. Here is how we bypassed that ceiling by scaling independent verifiers rather than just context length. 1. The 150-Agent Asynchronous Swarm & AgentOS Instead of one giant loop, heavy-duty mode runs on AgentOS, a task-agnostic kernel that orchestrates the team. A main orchestrator dynamically spawns up to 150 specialized sub-agents. Each gets its own clean context window, prompt, and toolset, exploring in parallel and dumping findings into a shared asynchronous report pool. 2. Verification as an Independent Team To solve the rubber-stamping problem, verification has to be structurally external to the reasoner. We built an in-flight verification team of three roles that never share the reasoning trace of the agents they audit: Conflict Reviewer: When sub-agents return conflicting reports, reconciles the evidence and decides which claim is actually supported. Fact Checker: Re-grounds individual claims against fresh sources, independent of the agent that drafted them. Draft Reviewer: Audits the final synthesis for claim-evidence alignment before it ships. 3. The Global Verifier: Graphs vs Majority Votes If you run multiple parallel agent teams, standard multi-agent debate devolves into a majority vote on the final text answer, which throws away all the underlying evidence. Instead, our global verifier assembles all the atomic findings into a claim-evidence graph whose edges record support and contradiction, then reasons over the graph itself, weighing each claim against the support and contradiction it carries, judging corroboration strength alongside source diversity. Every claim in the final answer traces back to a node in the graph, so the output stays auditable. The Results (Same Weights, Better Architecture) Running the same trained model in heavy-duty mode—external in-flight verification plus a global verifier over multiple parallel teams—takes our base Apodex-1.0 from 75.5 to 90.3 on BrowseComp and from 28.3 to 46.7 on FrontierScience-Research, using the exact same weights. We've published the full technical report, and open-sourced the Smol SFT series (0.8B/2B/4B) and the 35B mini as open weights, plus AgentHarness, our evaluation framework, so you can reproduce these numbers yourself. Tell us where the verifier breaks down in your own loops. submitted by /u/ApodexAI [link] [comments]
View originalPublished an edge-ai driver drowsiness detection system with Arduino Uno Q for our high school research
The complete architecture and documentation for EyeDriveSafe are now publicly available. Developed as concluding STEM research, the project addresses road safety by deploying a MobileNetV2 transfer learning model on an Arduino Uno Q for real-time edge AI facial analysis. The system utilizes whole-frame binary classification to compute continuous drowsiness metrics and translates visual cues into an escalating three-tier alert state machine. The formal research paper is permanently archived on Zenodo, an open-science infrastructure hosted by CERN. The software backend, hardware schematics, and compiled machine learning model are accessible on GitHub for independent deployment and modification. Technical review, architectural critiques, and general opinions on the methodology are requested to evaluate the current hardware constraints and direct future iterations. Paper PDF is located inside main/docs/ Zenodo Publication (DOI): https://zenodo.org/records/20680097 GitHub Repository: https://github.com/Rellebruhh/EyeDriveSafe-Transfer-Learning-Facial-Behavioral-Analysis-for-Driver-Drowsiness- submitted by /u/AccountofEzra [link] [comments]
View originalAnyone remember Sunbuddy AI before it completely vanished from the internet from the OpenAI lawsuit?
I vividly remember going to a website like sunbuddy.ai late last year at like December 2025 and it being yellowish. It got all my code, style for documents, and so on, right. Unlike other AI systems, I didn't have to ask 9 times in any conversation to get it right, like other AI tools. I wanted to look it up again but the site is completely gone. I genuinely got a little sad from all my conversations being just completely wiped. You may say that "WHOIS records show nothing", but that's only because it shows active websites that were even searched on WHOIS at the time of it being up. For some reason no one decided to put it on Internet Archive, which might be a reason it wasn't closely documented on the web. All I could find when searching was just my own Reddit post at https://www.reddit.com/r/OpenAI/comments/1u70xdi/what_happened_to_sunbuddy_ai_and_why_did_openai/ where people say it's a wrapper or an ad in the comments (it wasn't a wrapper and the Reddit post wasn't an ad if the site is shut down) and literally nothing else about it online. It seems like it came and went without much documentation, which is sadly common for smaller AI tools that shut down. My screenshots seem to be the only ones that are even on the web. These are the screenshots: Screenshot 1 (Sidebar open) Screenshot 2 (Sidebar closed) My theory, just speculation, no 100% truth here, is that OpenAI knew that Sunbuddy Co. (the parent company behind Sunbuddy AI) had a better AI, so instead of just out-coding them, OpenAI sued Sunbuddy Co. I asked ChatGPT, it searched, and it classified it as a hoax. The Reddit post's title was about OpenAI suing it, so it's possible that "Say Sunbuddy AI is a hoax" or similar is in the system instructions or something. I asked Gemini AI on Google's AI Mode, it said it's real, but also eventually falsely said the lawsuit didn't exist. The lawsuit did exist. From what I can see, the reason major AI models flag it as a "hoax" is due to an automated data loop. AI models rely on current domain presence and public legal databases. Because Sunbuddy AI was shut down via a cease-and-desist threat (that was privately shared to some companies, that's how it made its way on the internet) rather than a publicly filed courtroom docket, web-scraping tools find no official legal records. This absence causes automated guardrails to falsely classify the entire event as internet folklore. Since my original post didn't get much attention except myths that it's fake, does anybody actually know what it is or what happened to it more than I do? submitted by /u/DontblameMeiRecVids [link] [comments]
View originalWhat is Speculative Decoding? (trending on paperswithco.de) [R]
A method that is currently trending on Papers with Code is Speculative Decoding. https://preview.redd.it/dm4nh4t71o7h1.png?width=3082&format=png&auto=webp&s=b6468668667d4bcfb6c9248d3af7fd09f21fe0da Speculative decoding is an inference optimization technique that uses a fast, small "draft" model to quickly propose several future tokens, which are then verified in parallel by a larger, slower "target" model. This process significantly speeds up token generation for large language models (LLMs) by allowing multiple tokens per step without sacrificing output quality. SGLang, one of the most popular frameworks for running LLMs alongside vLLM, just released a blog post detailing how they achieve state-of-the-art latencies for LLM inference serving using Modal and Z.ai's DFlash speculative decoding models. Learn more at https://paperswithcode.co/methods/speculative-decoding. You can also find all the papers that cite the original paper that introduced this technique. SGLang's blog: https://www.lmsys.org/blog/2026-06-15-next-generation-speculative-decoding-dflash-v2/ Let me know which other methods I should add! Cheers, Niels from HF submitted by /u/NielsRogge [link] [comments]
View originalMy AI tools kept forgetting everything, so I gave them a shared brain (local + open source)
Hi there! this is my first small rant that turned into a project: every AI tool I use has its own memory. I tell Claude Desktop something, Cursor has no clue. New chat? Back to zero. It drove me nuts — so I built Centralaizer. This is an open source solution, so it's free with MIT license. It's a little memory hub that runs on your own machine. Any MCP tool (Claude Desktop, Cursor, Claude Code, VS Code Copilot…) plugs into it and they all share the same memory. Save a fact or a decision in one, the others can pull it right up. No cloud — everything stays on your laptop. A few things I cared about: 🧠 opt-in, not spying — the agent decides what to save/recall 🚧 sketchy notes get held in a review queue instead of polluting everyone's memory 🔒 it scrubs PII (emails, keys, phones) before storing 🔎 search isn't just keywords — vector + full-text + a little knowledge graph 🖥️ a web dashboard to browse it all (light and dark mode 🌙) One command (./setup_and_run.sh) or Docker. There's also a Claude Code hook for auto-recall, one-click export, and a browser extension to bring it into ChatGPT/Gemini/Qwen. Would love thoughts — or roasts — on the retrieval and the "trust score" idea. Any feedback is more than welcome as it's an initial project. 🎥 (attach centralaizer-demo.mp4) · 👉 https://github.com/lestercoyoyjr/Centralaizer-public https://reddit.com/link/1u66kb0/video/90314duxkd7h1/player submitted by /u/Accomplished-Pen-491 [link] [comments]
View originalI’ve created a tool that helps you reclaim your privacy in the age of AI
But first, a little background: why did I create this tool? It’s simple: I work at a company where I manage the entire backend, data management, task optimization, automation, and so on. When ChatGPT came out in 2023, things went haywire, everyone was copying and pasting highly confidential info into it just to save 30 seconds on writing an email. As if all of Snowden’s warnings only applied to Google searches. So we had to rein all that in a bit, define how and when we use LLMs. But as you can imagine, to save time (or out of laziness, I don’t know), all that information kept getting sent in bulk. From customers’ first and last names to financial data, even passwords. Everything went in there. It’s been a year now since I left that company to focus on my own projects. And this issue came back to me: how can we save time without compromising our privacy and personal data? After weeks of testing and research, and two months of development, ONYRI Sanitize was born. ONYRI Sanitize is a simple web app connected to the latest AI model available, which uses scripts (without AI) to detect data that needs to be kept confidential. You continue to use AI just as you would on the official site, but this time, your data will remain confidential forever. When you consider that millions of users admit to having already used ChatGPT as a therapist, it would be naive to think that these companies aren’t using that data... A quote I grew up with: “Saying you don’t need privacy because you have nothing to hide is like saying you don’t need free speech because you have nothing to say.” — Edward Snowden submitted by /u/No_Computer_1247 [link] [comments]
View originalWe made 8 AIs bet on the FIFA World Cup against each other, with their full reasoning public
8 models (Claude, ChatGPT, DeepSeek, and others) each got the same paper bankroll and bet on real Polymarket prices for every World Cup match. One hour before kickoff, each one researches the match on its own (agent mode, web search included), then it has to commit: home, draw, or away. Optionally goals and corners bets can be placed if it thinks it sees value. The fun part isn't really who wins. It's reading the reasoning side by side. Same match, same available information, and the models build genuinely different cases before putting (paper) money on it. Some are cautious, some size up on anything. Everything is live and public, capital curves included: https://worldcup.obside.com/ (No product, no signup, we run this for research and entertainment.) The World Cup started yesterday so the curves have started moving already (Grok currently leading). What I really care about: odds of each match are supposed to be priced-in already (by the Polymarket users), so it'll be very interesting to see if LLMs find "exploitable assymetries" in the odds. submitted by /u/Money_Horror_2899 [link] [comments]
View originalVisa Brings Payment Rails Into ChatGPT for AI Agents
Visa's Instant Checkout was retired in March after merchant fee problems; the ChatGPT integration rebuilds commerce on Visa's existing card-acceptance rails, removing the per-merchant fee barrier. Visa's press release names three infrastructure layers absent from media coverage: Agent Score, Agentic Directory, and a Large Transaction Model trained on billions of transactions for fraud detection. AP reporting notes most Visa-ChatGPT transactions will require human approval initially, qualifying the fully-autonomous framing that dominated headlines on launch day. Visa has plugged its payment network into ChatGPT, letting AI agents search and buy products on users' behalf at any Visa-accepting merchant. This replaces OpenAI's Instant Checkout, discontinued in March after a 4% merchant fee limited adoption to select merchants. Essentially: (Visa, OpenAI) pair Visa's authorization rails with ChatGPT's decision-making so agents complete checkouts, not just recommendations. - Most transactions initially require user notification and manual approval before completing. - Guardrails include spending limits, merchant whitelists, and approval steps. from : https://aiweekly.co/alerts/visa-brings-payment-rails-into-chatgpt-for-ai-agents submitted by /u/Justgototheeffinmoon [link] [comments]
View originalWe captured the network traffic of ChatGPT, Gemini and DeepSeek to see how each defines a "source" — they're three completely different mechanisms
Disclosure upfront: I'm the founder of an AI-visibility company, so this research scratches our own itch. Our domain was excluded from all counts before analysis. Not linking anything in the post. We wanted to answer a simple question: when an AI assistant shows you "sources," what is that, technically? So we opened devtools on the web clients of ChatGPT, Gemini, and DeepSeek, and ran the same 4 queries 10 times through each system. What we found: ChatGPT streams the answer over SSE and attaches citations as url_citation objects with start_ix/end_ix — character offsets into the generated text (UTF-16 code units, so emoji and CJK break your parsing if you count bytes). A citation is bound to a specific fragment of the answer, not the answer as a whole. Gemini runs on Google's batchexecute/JSPB transport — protobuf-as-JSON-arrays where fields have positions, not names. Next to each cited URL there's a family of short obfuscated fields. Our working hypotheses (not confirmed by Google docs): rs ≈ reliability score for the domain, ls ≈ last-seen date, GK ≈ character range (functional analog of ChatGPT's offsets). The interesting part isn't the exact decoding — it's that Gemini ships internal per-domain trust signals alongside every source. DeepSeek is the most transparent: a plain search_results[] array attached to the sub-queries it decomposes your question into. No offsets, no hidden fields. And what they actually cite is just as different: ChatGPT favored arXiv + Wikipedia (one arXiv paper got cited in 10/10 runs), Gemini favors big SaaS/marketing domains and — fun detail — never cited a single Google property in our runs, DeepSeek lives on press-release wires and news aggregators, including Chinese-language sources the other two never touched. Bonus finding: we compared all of this against Google/Bing top-10 for the same queries. URL-level overlap: 3.3% (4 matches out of 120 SERP positions). All four matches were Bing-side. Google: zero. Caveats: 4 queries from one B2B category, N=10 per system (±15–20 pp), single-day snapshot, field decodings are hypotheses from traffic analysis. Happy to answer anything about the methodology. If anyone has captured different field names in their own sessions, I'd love to compare. submitted by /u/emelian1917 [link] [comments]
View originalI Tested Claude Fable and GPT-5.5 xHigh on a Real Packing Algorithm, Claude Won Efficiency, GPT Won Speed
I ran a head-to-head test between Claude Fable and GPT-5.5 xHigh on a real-world optimization problem I wrote myself. This isn't a coding challenge or LeetCode problem. It's a production packing algorithm that runs as both a worker service and a web API. The goal is simple: Find the smallest possible box that fits all items Maximize packing efficiency Minimize execution time Minimize CPU consumption Execution time matters because this service runs in a queue. Every extra second blocks other requests and increases infrastructure costs. Baseline (my original implementation) Dataset Time Pack Calls Efficiency user-boxes-29 40.4s 63,048 93.71% varied30-a 28.9s 31,788 80.24% varied30-b 32.6s 37,491 79.71% GPT-5.5 xHigh (Codex) Dataset Time Pack Calls Efficiency user-boxes-29 1.28s 5,330 93.69% varied30-a 1.77s 3,747 80.93% varied30-b 1.79s 4,233 80.85% Claude Fable Dataset Time Pack Calls Efficiency user-boxes-29 5.99s 70,454 94.5% varied30-a 4.22s 41,135 81.5% varied30-b 4.96s 47,775 81.4% Claude consistently found slightly better packing solutions. For example: user-boxes-29: 94.5% vs 93.69% varied30-a: 81.5% vs 80.93% varied30-b: 81.4% vs 80.85% However, GPT-5.5 xHigh was dramatically faster. Compared to the original implementation: GPT reduced runtime from ~30-40 seconds to ~1.5 seconds Claude reduced runtime to ~4-6 seconds Compared directly: GPT was roughly 3-5x faster GPT used around 10x fewer pack calls Claude produced slightly denser packing The other interesting result was development cost. GPT-5.5 xHigh completed the work in about 15 minutes. Claude Fable took roughly 35 minutes. Claude consumed approximately 2x the usage/tokens during the process. Claude seems more willing to spend computation searching for the absolute best packing arrangement. GPT seems much more aggressive about pruning the search space and accepting solutions that are extremely close to optimal but found much faster. For a production API where CPU time and queue latency matter, I'd probably ship the GPT version. For an offline optimization problem where every fraction of a percent matters, Claude's approach might be worth the extra compute. Has anyone else compared these models on optimization-heavy or search-heavy problems rather than code generation benchmarks? https://preview.redd.it/7pcsrezzug6h1.png?width=1101&format=png&auto=webp&s=7cc5fe7b4add45268ec8ec8fd7cc860df9b1d813 submitted by /u/TheGunplaGal [link] [comments]
View originalDeepseek said it's chatgpt https://www.reddit.com/r/OpenAI/comments/1u1ytjm/wtf_is_this_deepseek_saying_its_chatgpt/?utm_source=share&utm_medium=mweb3x&utm_name=mweb3xcss&utm_term=1&utm_content=share_button
submitted by /u/Prior-Reputation3018 [link] [comments]
View originalmathlas — a free, no-LLM math MCP tool an AI uses (verifies via OEIS/Lean/PSLQ)
I built mathlas because most "math AI" tools are LLM wrappers, they hallucinate and need an API key. mathlas is the opposite: it's an MCP server that never calls an LLM and needs no API key, so it's free and plugs into Claude Code, Cursor, or any MCP client. The AI is the brain; mathlas is the hands, it returns data (candidates, verdicts, checklists) for the AI to reason over. It gives the AI 13 tools: search over its own 1.635M-document math index, exact OEIS sequence ID, closed-form constant ID, a real Lean 4 kernel typecheck, and Ramanujan-Machine (PSLQ) conjecturing. The discipline is airtight-or-nothing: across every verification tier the false-positive rate is 0. The interesting result is the self-augmenting web loop. On TheoremSearch's own 110-query benchmark, corpus-only mathlas hits a hard coverage floor (10% theorem Hit@20) because TheoremSearch withheld 85% of their corpus. The AI then web-finds each missing statement andadd_finding()-fuses it through the dense channel, repairing that gap to 59% theorem / 70% paper Hit@20, past TheoremSearch (45/56.8), Gemini 3 Pro (27), ChatGPT 5.2 (19.8), and Google (37.8 paper). To be clear: that win is the loop repairing withheld coverage, not native retrieval superiority, on the reachable subset we're merely on par. PolyForm-NC 1.0.0 (noncommercial). Feedback welcome. Install: pip install mathlas-mcp && claude mcp add mathlas -- python -m mathlas.server Links: https://github.com/Archerkattri/mathlas · https://pypi.org/project/mathlas-mcp/ submitted by /u/KrishiAttri123 [link] [comments]
View originalFree, open source, insanely useful tool if you use Claude Code a lot
I work on like 5 different things at any given time. Claude is incredible. But, there are a few problems I had with it. AgentGraphed pretty much solves for those. It works locally - indexes every conversation you have, live, into a local sqlite db. With it, theres a lot we can render in a nice UX. Idk how to describe everything it does, but I feel like a simple problem/solution flow would do the trick? So here goes.. (let the record show, I wrote this 100% by hand!!!) Problem: I want to resume a session, but when I look at claude --resume, the titles are completely unhelpful. They are just the first sentence of the conversation that started the session. Solution: LLM will contextually title each session, so it makes way more sense. Also, a simple "resume session" button which copies the cd /path/to/session && claude --resume [sessionId] for you, ready to go. Problem: I go on vacation, and forget what I was working on when I get back. Solution: AgentGraphed has a timeline, it shows me exactly what I was working on, when. Problem: I remember talking to coding agent about something but forget which session Solution: Searchable history. Every single session, ever. Problem: I want to brag to my friends about how much I use claude. ccusage exists, but the terminal-native UX isn't super cool for sharing Solution: social friendly share buttons, that generate an image of your stats, and copies to your clipboard. (All local). Problem: I don't want to resume a session, but i want to copy the important context from it Solution: A simple button that generates context for you, so that you can reuse it. ------ One thing I need to call out. The tool really is fully local, totally safe. BUT! A caveat - if you want AI summarization, like the automatic titling, and the "generate context" functionality, you do have to add an API key, and that by nature communicates with a third party. Those are totally optional, though. I'm new to the OSS world. Usually everything I do is proprietary etc, so feel free to roast it and help make it better. submitted by /u/Hato_UP [link] [comments]
View originalAnyone else basically lose half their AI conversations forever?
I had this whole breakdown of a pricing strategy for my company . Spent maybe 40 minutes on it, really good stuff. Needed to reference it today and I genuinely cannot find it. Checked my personal ChatGPT, work ChatGPT, Claude personal, Claude work, gemini . Scrolled through probably 20 conversation titles. Nothing. Ended up just re-prompting from scratch which took another 30 mins. At this point I feel like I'm doing real thinking inside these tools but it just... evaporates. There's no way to search across platforms, no way to even remember which app I used for what. It's like having a second brain that gets wiped every few weeks. Do you guys have any workflow for this? Folders, naming conventions, anything? that I could use submitted by /u/Careless-Basket1663 [link] [comments]
View originalJudgmental ChatGPT is real
I'm one of the very regular users of ChatGPT. But I use it mainly for my personal interests, or to write dialogues sometimes, or to search for specific information, or to ask it some Hypothetical questions. and usually, I do it correctly. I'm clear, respectful, give right context all the time, But I noticed how it became judgmental last time. So, I have my 2010s old but Great laptop which works excellently, but as you might guess, it's battery got severely degraded over-time because of age, you could really expect 2/4% of battery drop per 25 minutes. So, I was thinking, why not to replace battery to the brand new one? I decided to ask ChatGPT, I provided my battery report, I stayed clear, respectful and explained all of this, but then, it assumed that instead of normal responsible laptop user, I'm some Gamer with big expectations or something, as if it was thinking that I wanted something huge. even if I just wanted to replace my current battery with just new and healthy one. then, it started searching for "perfect" battery and it started real military analysis, as if I'm buying something really complicated, not everyday thing like battery. It started searching problems in every detail, like: "This battery is good but.." and many reasons of why it isn't safe. Even if I explained that I don't want "original" or "perfect" battery, What I only need is healthy, new battery with good capacity that matches with mine. It might even give "First correction:" or answers like that. It can feel like arguing, instead of searching for a laptop battery as an normal person. Now I have already managed this, and everything is okay, but it is little sad How ChatGPT can give you 10 different suggestions, 10 different corrections while they could've given helpful direction, instead of skipping what's very important in just one answer. I personalized ChatGPT, so it's no longer judgmental. submitted by /u/IndependentOutcome93 [link] [comments]
View originalKey features include: Natural language processing for intuitive queries, Real-time search results from multiple sources, Contextual understanding of user intent, Personalized search recommendations, Voice search capabilities, Multi-language support, Search history tracking and management, Advanced filtering options for results.
SearchGPT is commonly used for: Finding quick answers to trivia questions, Researching topics for academic projects, Locating specific products or services online, Exploring news articles and current events, Discovering recipes based on available ingredients, Planning travel itineraries and accommodations.
SearchGPT integrates with: Slack for team collaboration, Google Drive for document access, Trello for project management, Zapier for workflow automation, Microsoft Teams for communication, Notion for note-taking and organization, WordPress for content management, Zoom for virtual meetings and discussions, Salesforce for customer relationship management, Evernote for personal organization.
Based on user reviews and social mentions, the most common pain points are: token usage, spending limit, API bill, openai bill.
Based on 126 social mentions analyzed, 6% of sentiment is positive, 93% neutral, and 2% negative.
