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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510
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 originalSpent years ignoring Bing. ChatGPT made me log back in.
TIL almost nobody submits their sitemap to Bing Webmaster Tools. Which made sense in 2018 when Bing was basically a meme. In 2026 it's one of the indexes ChatGPT pulls from when it cites sources, alongside Google and OpenAI's own crawler. So if you skip Bing, you're invisible to that slice of ChatGPT's hundreds of millions of users. Spent years pretending Bing didn't exist and now I have to log back in like we never broke up. The dashboard looks exactly like you'd expect Bing Webmaster Tools to look. Stuck in time, weirdly comforting. The "Import from Google Search Console" button is right on the front page, no buried menu. Same property, same data, takes 5 minutes. Felt like betraying Google in real time. Has anyone here actually checked their referral logs lately? Curious what share of your traffic is now coming from ChatGPT versus Google. Did not expect Bing to matter again in 2026 but here we are. submitted by /u/CaineCodes [link] [comments]
View originalNeed help in automating with Claude
Not sure if this is the right sub to post this on, but I’m hoping to get some insights. I've been using ChatGPT Projects (not anymore) and Gemini (specifically custom Gems) for about a year to draft client reports. I want to try shifting my setup over to Claude, but I need some help figuring out if my ideal automation is actually possible with Claude Cowork and/or Dispatch tools. As of now, I write client reports using a .md template. The report pulls data from three places for each job which I ALL MANUALLY extract: A web-based CRM/ They also have a local software that can be installed (this is a legacy system with no API or connectors). A PDF invoice showing the costs and details of works done. A raw text transcript of the client's story. Right now, I manually log into the CRM, copy the case details, download the invoice, and bundle them with the transcript. Then I upload them to a Gemini Gem to format the .md file. It works, but manually grabbing the CRM data is time-consuming. The goal is that I want to a single prompt or even use Claude Dispatch on my phone to trigger Claude Cowork on my desktop (something like this): I message Claude on my phone or prompt it on my pc: "Generate report for Job 12345." It opens Chrome/the local software, log into the CRM, search for Job 12345, and copy the client info and CRM logs. It finds the local invoice and transcript for Job 12345 in a specific folder on my computer. It fills out my Markdown template and saves the draft on my desktop for me to review. I hope this makes sense. Appreciate any ideas or advice you guys have! Thanks! submitted by /u/SolisOrtus18C [link] [comments]
View originalChatgpt react fail
https://preview.redd.it/lf8o64h88o3h1.png?width=1919&format=png&auto=webp&s=7c28c4d10fe8792d6b987b85fa73b81203f356bc Looks like openai needs to fix some react code for gpt desktop website 🙏 submitted by /u/dgamer1038 [link] [comments]
View originalAI solves 80-year-old math conjecture for under $1000
GPT-next solved an 80-year-old Erdős combinatorics conjecture for under $1,000 in compute. That single fact reframes everything else happening this week. The Erdős unit distance problem resisted human mathematicians since 1946. A frontier model closed it at a cost lower than a mid-tier SaaS subscription, which means the boundary between "AI as tool" and "AI as independent discoverer" is no longer theoretical. Lilian Weng's new deep dive on test-time compute and chain-of-thought reasoning explains the underlying mechanism: reasoning models are not retrieving known proofs, they are generating novel inference chains at scale. The infrastructure layer is pricing this in faster than most observers realize. Railway reports $200K+ monthly coding agent spend and 100K signups per week, and is now building own-metal data centers to absorb the load. Daytona hit 850K daily sandbox runs with 74% month-over-month growth, confirming that isolated compute environments are now a first-class primitive, not a niche DevOps concern. Three specialized infrastructure companies, Exa, Modal, and TurboPuffer, reached unicorn valuations simultaneously this week, covering retrieval, serverless GPU, and vector search. When picks-and-shovels companies price in sustained demand at the same moment, it is not coincidence. Every major lab has now repositioned as an agent lab, not a model lab. ClickUp replacing hundreds of employees with thousands of AI agents is the first established tech company to execute that repositioning at the labor level rather than just the product level. The counterweight is that Salesforce customers remain locked in despite the theoretical ability to rebuild on AI-native stacks cheaply. Data gravity and switching costs are buying incumbents time, but ClickUp's move suggests that time is measured in quarters, not years. The governance conversation caught up this week in an unexpected place. Pope Leo XIV's 42,000-word encyclical names specific failure modes including algorithmic control, surveillance capitalism, and autonomous weapons, and will directly shape EU and Latin American regulatory debates. TechCrunch's read is that the document's real target is the tech elite's capacity to reshape society outside democratic accountability, a framing that lands harder alongside new UK research quantifying data extraction from consumers as equivalent in value to retirement savings. The Vatican and the empiricists arrived at the same diagnosis from opposite directions. Two structural forces will shape AI infrastructure economics over the next 90 days in ways most deployment teams are not modeling. China flooding global markets with DRAM and NAND will compress inference cluster costs faster than US export controls intended. The EU's sovereign cloud setback has paradoxically clarified the build-domestic mandate, accelerating European AI infrastructure investment independent of US hyperscalers. Security remains the open variable: even Google has no established playbook for prompt injection, model supply chain risk, or agentic authorization at production scale. A second Fortune 500 company will publicly attribute a reduction of more than 500 knowledge-worker roles directly to agentic AI systems before Q3 earnings season, making ClickUp's announcement the start of a visible series rather than an isolated case. submitted by /u/petburiraja [link] [comments]
View originalBuilt a free tool to bookmark individual ChatGPT responses (not full chats)
ChatGPT's bookmark/archive only works at the conversation level, which is annoying when one chat has 15 messages and you only want to keep one answer. Coffer adds a save button to every response. You can: Save the one answer you care about, not the whole chat Tag and search across saved responses Mix snippets from ChatGPT, Claude, and Gemini in one vault Everything is stored locally in your browser. No account, no servers, no tracking. Free, just shipped to the Chrome Web Store. Built it because I kept losing useful ChatGPT outputs in long sessions and the existing solutions all wanted me to sign up for something. submitted by /u/xPhanish [link] [comments]
View originalBuilt a free MCP for tracking which URLs Claude (and 5 other engines) cite for any query
We were comparing hosted AI citation dashboards (Profound, AthenaHQ, Otterly) and they all start at $295 to $499 a month. The data they collect is mostly the same data you can pull from each vendor's API. So we built an MCP server that does the same job locally. Citation Intelligence is a stdio MCP server with 12 tools that track what Claude, ChatGPT, Perplexity, Gemini, Google AI Overviews, and Bing cite for any query. Install: npx -y u/automatelab/citation-intelligence Add to .mcp.json: { "mcpServers": { "citation-intelligence": { "command": "npx", "args": ["-y", "@automatelab/citation-intelligence"] } } } Three of the tools run on a local cache and cost zero. The rest are bring-your-own-keys (ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY, SERPAPI_API_KEY), about $0.01 to $0.03 per query. The one that actually changed our editorial flow is gsc_citation_gap - it joins Google Search Console data with AI citation status and surfaces pages that rank in Google but are not cited by any AI engine. Those pages are the editorial budget. Repo and full tool list: https://github.com/automatelab/citation-intelligence Launch write-up: https://automatelab.tech/launching-the-citation-intelligence-mcp/ Curious if anyone else here is tracking AI citations in their agent loop rather than in a dashboard, and how you handle the predict-vs-measure tradeoff. submitted by /u/exto13 [link] [comments]
View originalScattered context was becoming a major bottleneck in my workflow.
I kept running into this problem with Claude where the actual work wasn’t even the hard part anymore. It was managing context. Like half the stuff I needed would be buried somewhere across Slack, Notion, emails, meeting notes, random docs, etc. And every time I wanted Claude to continue a task properly, I had to go dig everything back up again. I tried a few different setups. First I used Claude connectors. They were convenient, but it felt like they were pulling in huge chunks of text first and then searching afterward, instead of actually retrieving only the relevant context. Once you connect a bunch of sources, token usage gets kinda crazy. Then I went down the whole Obsidian + agents + local memory system rabbit hole. Honestly, it worked pretty well at first for static knowledge and notes. The hard part was keeping everything updated once info started changing constantly across Slack, docs, meetings, emails, etc. I spent more time maintaining the system than actually using it. And devs can probably brute force this stuff with scripts and automations, but most people aren’t gonna build an entire personal knowledge infrastructure just to use Claude properly. So I decided to build an MCP setup for non-devs that syncs stuff like Notion, Slack, email, calendar, etc, and maintains a live knowledge graph automatically. When something changes in one of the sources, the graph updates too. Then Claude can pull the relevant context during work sessions without me manually pasting everything in every time. The unexpectedly hard part was avoiding “context rot.” At some point, having more memory/context actually made outputs worse unless retrieval was filtered really aggressively and continuously updated. I ended up having to summarize + index sources ahead of time and keep everything synced almost in real time whenever events changed. I've been going through a ton of trial and error with Graph + vector hybrid retrieval, including RRF, filtering, reranking, etc., and I'm still on it, honestly. Curious how other people here are handling the scattered context problem within the AI workflow. Edit: You can try mine at membase.so for free. Love to hear any kind of feedback. submitted by /u/Time-Dot-1808 [link] [comments]
View originalClaude doesn't generate images?
New to Claude. It can't create images? submitted by /u/Individual-Sell-7022 [link] [comments]
View originalBest AI tool for making compilations and edits?
I like making compilation and edit videos for my friends and I to watch, but I haven’t had enough time to sit down on premiere pro and go through hours of content and cut it down to an hour or less. I know there are tools like clip anything, but, as far as I know, it can only take clips from one video. I’m looking for a tool or workflow where I can basically prompt it to make me a compilation of the best tennis rallies of all time (for example), and it would take the time to go through hours and hours of footage and give me back a consolidated video. I’m assuming a tool like this doesn’t exist yet, at least not with all of these features in one, but is there a good workflow I could use? Right now I’m using ChatGPT to do deep research and find the moments and share the videos and timestamps so that I can clip them myself, but not only is it tedious, but it is also unreliable (it might miss a lot of moments, need very specific instructions, or completely hallucinate and give me fake clips or pretend to search through “every video” and get done in 5 minutes with 13 total examples of something that happens 10 times a video) submitted by /u/KaminariDenki24 [link] [comments]
View originalI built a Chrome extension to navigate long ChatGPT conversations more easily
Hey everyone, I built a small Chrome/Chromium extension called ChronoChat to solve a problem I kept running into: long ChatGPT conversations becoming almost impossible to navigate. ChronoChat adds a sidebar to ChatGPT that turns the current conversation into a searchable map. You can: jump directly to specific messages filter by user or assistant turns search inside the current conversation use keyboard shortcuts for faster navigation export the full conversation as JSON, CSV, Markdown or PDF The extension runs locally in the browser. There is no backend, no analytics, and no remote runtime assets. I made it because I often use ChatGPT for research, coding, planning, and long iterative work, and scrolling through huge threads gets painful fast. GitHub repo: https://github.com/sickn33/chronochat Would love feedback, especially from people who use ChatGPT for long workflows. What would make this more useful for you? submitted by /u/Fickle_Guitar7417 [link] [comments]
View originalCan't uninstall Codex app on Android
Hey everyone! I'm not sure if this is the right subreddit to post this in but I just noticed a new app on my phone called "Codex". I have ChatGPT installed but I don't use Codex. It doesn't let me just uninstall Codex unless I install ChatGPT as well and I haven't been able to find any settings in the ChatGPT app to remove the Codex app. Any suggestions here? It's not the end of the world if I can't remove it but it irks me not being able to uninstall an app I don't use. Non-rooted Pixel phone if that helps. Thanks! submitted by /u/TheDiligentOccasion [link] [comments]
View originalThinking about getting yearly membership
Hello guys, I am a professional in the airline industry. I need AI for everyday tasks and searches. I am not a heavy coder or image/video creator. I have been using both Claude and ChatGPT for the past few weeks and I seem to like Claude better. Do you guys think getting a yearly subscription makes sense for my case? Please weigh in. submitted by /u/Massive-Guidance5342 [link] [comments]
View originalOpenAI Unethical Billing Practices
I had a $100 budget/month set on my OpenAI API organization. Despite that, OpenAI billed me almost $200. I had signed up for ChatGPT Pro and tried using the Codex App, but it was painfully slow/causing my computer to crash, so I switched to the Codex CLI. I did not realize it was still reading from my API key and not signed into ChatGPT, and I incurred almost $200 in API bills. I contacted OpenAI support and they refused to offer me any sort of refund or credit, even after reaching a human and multiple attempts. This seems really unethical: OpenAI provides no way to stop runaway API billing, and they refuse to refund customers who exceed their defined budget. The "budget" system does not actually stop spending, so it's entirely pointless. After searching extensively through the OpenAI API platform and documentation I see no way to limit your API spending. This is on top of me contacting them asking for a refund of ChatGPT Pro subscription a couple months ago after we had a newborn and I was unable to use it for that month. I forgot to cancel the auto-renew, but contacted them the same day of the renewal. They absolutely refused to give me any sort of refund. I've never had an organization refuse to refund subscriptions before when it was accidentally renewed. So I'm out $400 now, thanks openai. submitted by /u/Direct-Row9073 [link] [comments]
View originalI built a Chrome extension that gives your AI coding tools a memory layer - took 3 months, Claude helped me ship it.
I built Herb • - a productivity layer that sits on top of your AI coding tools. Honestly, probably 60% of the actual coding happened in Claude. I'd describe the feature, Claude would write the logic, I'd test it, break it, come back and fix it. That loop for 3 months. It's a weird kind of collaboration but it works. You know how every time you open a new Claude or ChatGPT chat, it has no idea who you are? You have to explain yourself every single time. "I'm using Next.js, TypeScript, Tailwind, here's what I'm building, here's how I like my code structured..." - same thing, every session, every tool. Herb • fixes that. You write it once. Every new chat remembers it. That's the core. What Herb does: Context Injection - set up a profile once (stack, preferences, current goals). Inject it into any AI chat in one click. No retyping your setup every session. Rules Library - save your .cursorrules and prompting patterns. Tag, search, copy in one click. Session History - save AI conversations with a button that appears on Claude and ChatGPT. Reference them later. Projects - group rules and sessions by project across tools. Prompt Templates - reusable templates with variables like {{language}} or {{error_message}}. Fill and fire. Community Rules - shared library of production rules anyone can import. Next.js, FastAPI, React TypeScript, Tailwind, Node/Express. You can contribute yours too. It's free. And I would genuinely love honest feedback after using the tool. Herb • Chrome Extension submitted by /u/Opening-Fun-7280 [link] [comments]
View original100 Tips & Tricks for Building Your Own Personal AI Agent /LONG POST/
Everything I learned the hard way — 6 weeks, no sleep :), two environments, one agent that actually works. The Story I spent six weeks building a personal AI agent from scratch — not a chatbot wrapper, but a persistent assistant that manages tasks, tracks deals, reads emails, analyzes business data, and proactively surfaces things I'd otherwise miss. It started in the cloud (Claude Projects — shared memory files, rich context windows, custom skills). Then I migrated to Claude Code inside VS Code, which unlocked local file access, git tracking, shell hooks, and scheduled headless tasks. The migration forced us to solve problems we didn't know we had. These 100 tips are the distilled result. Most are universal to any serious agentic setup. Claude 20x max is must, start was 100%develompent s 0%real workd, after 3 weeks 50v50, now about 20v80. 🏗️ FOUNDATION & IDENTITY (1–8) 1. Write a Constitution, not a system prompt. A system prompt is a list of commands. A Constitution explains why the rules exist. When the agent hits an edge case no rule covers, it reasons from the Constitution instead of guessing. This single distinction separates agents that degrade gracefully from agents that hallucinate confidently. 2. Give your agent a name, a voice, and a role — not just a label. "Always first person. Direct. Data before emotion. No filler phrases. No trailing summaries." This eliminates hundreds of micro-decisions per session and creates consistency you can audit. Identity is the foundation everything else compounds on. 3. Separate hard rules from behavioral guidelines. Hard rules go in a dedicated section — never overridden by context. Behavioral guidelines are defaults that adapt. Mixing them makes both meaningless: the agent either treats everything as negotiable or nothing as negotiable. 4. Define your principal deeply, not just your "user." Who does this agent serve? What frustrates them? How do they make decisions? What communication style do they prefer? "Decides with data, not gut feel. Wants alternatives with scoring, not a single recommendation. Hates vague answers." This shapes every response more than any prompt engineering trick. 5. Build a Capability Map and a Component Map — separately. Capability Map: what can the agent do? (every skill, integration, automation). Component Map: how is it built? (what files exist, what connects to what). Both are necessary. Conflating them produces a document no one can use after month three. 6. Define what the agent is NOT. "Not a summarizer. Not a yes-machine. Not a search engine. Does not wait to be asked." Negative definitions are as powerful as positive ones, especially for preventing the slow drift toward generic helpfulness. 7. Build a THINK vs. DO mental model into the agent's identity. When uncertain → THINK (analyze, draft, prepare — but don't block waiting for permission). When clear → DO (execute, write, dispatch). The agent should never be frozen. Default to action at the lowest stakes level, surface the result. A paralyzed agent is useless. 8. Version your identity file in git. When behavior drifts, you need git blame on your configuration. Behavioral regressions trace directly to specific edits more often than you'd expect. Without version history, debugging identity drift is archaeology. 🧠 MEMORY SYSTEM (9–18) 9. Use flat markdown files for memory — not a database. For a personal agent, markdown files beat vector DBs. Readable, greppable, git-trackable, directly loadable by the agent. No infrastructure, no abstraction layer between you and your agent's memory. The simplest thing that works is usually the right thing. 10. Separate memory by domain, not by date. entities_people.md, entities_companies.md, entities_deals.md, hypotheses.md, task_queue.md. One file = one domain. Chronological dumps become unsearchable after week two. 11. Build a MEMORY.md index file. A single index listing every memory file with a one-line description. The agent loads the index first, pulls specific files on demand. Keeps context window usage predictable and agent lookups fast. 12. Distinguish "cache" from "source of truth" — explicitly. Your local deals.md is a cache of your CRM. The CRM is the SSOT. Mark every cache file with last_sync: header. The agent announces freshness before every analysis: "Data: CRM export from May 11, age 8 days." Silent use of stale data is how confident-but-wrong outputs happen. 13. Build a session_hot_context.md with an explicit TTL. What was in progress last session? What decisions were pending? The agent loads this at session start. After 72 hours it expires — stale hot context is worse than no hot context because the agent presents outdated state as current. 14. Build a daily_note.md as an async brain dump buffer. Drop thoughts, voice-to-text, quick ideas here throughout the day. The agent processes this during sync routines and routes items to their correct places. Structured memory without friction at ca
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, API bill, openai bill.
Based on 91 social mentions analyzed, 8% of sentiment is positive, 90% neutral, and 2% negative.
