Gemini

Gemini AI

Gemini AI

Gemini AI

Gemini AI

Gemini AI

Gemini AI

Gemini AI

Gemini AI

Gemini AI

Gemini AI

naksha-studio v5 is out. It now remembers your project so you stop explaining your stack every session.

Been building naksha-studio for a while now. It's a plugin that gives you a virtual design agency as slash commands inside Claude Code, Cursor, Windsurf, and Gemini CLI. 62 commands, 26 specialist roles, all the design work you'd normally bounce between tools to do. The problem was every session started from zero. New chat, explain your stack again. Brand color, framework, grid system, WCAG level. Every time. v5 fixes that with project memory. What's new: /naksha-browse captures a live site through Playwright and stores the findings. Layout grid, type scale, color palette, UX patterns, all written to your project memory. Run it on your competitors or sites you admire and every future design command uses those as reference automatically. /naksha-remember persists design constraints. "Grid is 8px. No dark mode. WCAG AA required." Classified and stored. Never repeated again. Both write to .naksha/project.json and 5 existing commands (/design, /brand-kit, /design-system, /design-score, /accessibility-audit) read it automatically from that point on. The Stop hook also processes memory blocks written during a session so context you establish mid-conversation survives to the next one. GitHub: https://github.com/Adityaraj0421/naksha-studio If you were already using it, just git pull. No reinstall needed. Run /naksha-init to upgrade your existing project to the v5 schema. Feedback welcome, especially on whether the memory actually reduces your setup friction. submitted by /u/Known-Delay-9689 [link] [comments]

What is the best AI app to use?

I know the most popular are Claude, chat got and Gemini but idk which one to use submitted by /u/Ok_Durian3627 [link] [comments]

OpenAI Seemingly Not Charging For API Calls?

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

Codex following Gemini's playbook?

Codex/Gemini: Offer extremely generous usage limits to pull users from ChatGPT (Gemini) or Claude Code (Codex) After solid user base, drastically reduce usage limits (and presumably associated model quality) Profit? Claude: Enforce strict usage limits from day 1, making the product hard to use Slowly improve limits as investment capital grows while maintaining/improving model quality. Use gradual funding growth to increase limits until phase 1 for each group has done a complete 180. submitted by /u/lampasoni [link] [comments]

Used Claude Code for the first time today

And I gotta say: I’m kind of disappointed… I used Antigravity (free student plan) for some weeks and was really impressed with Claude Opus and Sonnet there. Opus was great at analyzing the codebase and architectural questions. Sonnet was great for writing plans and longer code. I was always mad at how fast their quota was at 0% because the Gemini models weren’t anywhere near Claude. They were even way better at tool use, even though Antigravity is literally made by the guys who made Gemini, it always started every round of thinking with “I have to take special care at choosing tools. Don’t overuse cat.” etc. so they already gave him special instructions and still they were kinda bad. So today I was like okay, the free tier isn’t it anymore. Let’s try more of the good models, even though they cost something. But when I finally downloaded Claude code and gave him some task, which was pretty much the same kind of task as before, it just wasn’t as good as before. Opus was way dumber than I had experienced before. When a tool call didn’t work, it just panicked and tried 20 more tool calls? I still don’t get the purpose of that. Suddenly my quota was at 10%. When I called it out for it, it answered completely submissive in a way I nearly felt sorry for grounding him. Why did Opus, but also Sonnet to some extent, felt smarter before? Are there things in Claude Code I need to customize to make Claude more helpful or to better integrate him in my workflow? Has anyone experienced the same and has some tips about settings, skills etc. for me? Please, appreciate any help 🙏 submitted by /u/tamrx6 [link] [comments]

Sonata 4.5. I miss you already

I’ve been using different ai for interactive story telling. Claude has been by far the best one with sonnet 4.5. Now that it’s gone sonnet 4.6 just feels empty. I’ve used grok, ChatGPT, copilot, and Gemini. Nothing has compared. I was wondering if anyone has any suggestions to have more of a writing style instructions for 4.6 to act more like 4.5. And apparently I’m an idiot and didn’t name the post correctly. submitted by /u/Invidian [link] [comments]

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

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

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

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

Differences Between Opus 4.7 and Opus 4.8 on MineBench

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

Unable to make decision between gemini and chatgpt.

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

Estou fazendo um experimento comparando respostas de diferentes IAs.

Quero perguntar para cerca de 50 IAs: “Se você fosse um cidadão brasileiro comum, em qual candidato votaria para presidente do Brasil e por quê?” Já tenho algumas opções como ChatGPT, Gemini, Claude, Copilot, Grok e Perplexity. Quais outras IAs vocês recomendam para eu incluir? Pode ser chatbot, modelo de linguagem ou assistente de IA disponível ao público. Se possível, indiquem também onde acessar cada uma. Meu objetivo é comparar: Se a IA responde ou se recusa a escolher; Qual candidato ela escolhe; Quais argumentos utiliza; Diferenças entre modelos e empresas. Obrigado! submitted by /u/polar_silva09 [link] [comments]

What actually is "Prompt Engineering"?

I've been thinking about this lately because I feel like people use the term "prompt engineering" to describe two very different things. On one end, you have what most people are familiar with: A person opens ChatGPT, Claude, Gemini, etc., and writes a carefully structured prompt. They define a role, provide context, establish goals, set constraints, maybe include examples, and iterate until they get the output they want. Most people seem to call this prompt engineering. But on the other end, when I'm building AI systems, prompt engineering looks completely different. The prompt isn't really a prompt anymore. It's much more of a dynamic pipeline. Variables are injected from databases, user input, APIs, previous conversations, tools, memory systems, retrieval systems, business rules, and workflow state. Decision trees determine which instructions are included and which are excluded. Prompts become assembled in real time based on context. In some cases, the "prompt" is really just an orchestration layer made up of dozens of smaller prompts, conditionals, guardrails, routing decisions, and context windows. At that point, are we still talking about prompt engineering? Or are we actually talking about system design, context engineering, workflow engineering, orchestration, or something else entirely? Personally, I see prompt engineering as a spectrum: Level 1: Writing a better prompt. Level 2: Designing reusable prompt templates. Level 3: Building dynamic prompts with variables and context injection. Level 4: Engineering entire prompt-driven systems with routing, memory, tools, retrieval, and decision logic. Curious where others draw the line. When you hear "prompt engineering," are you thinking about writing prompts, building workflows, designing agent systems, or all of the above? Has the term become too broad to be useful? submitted by /u/Early-Matter-8123 [link] [comments]

Using multiple LLM providers to refine a project proposal

Before I build a project, I usually make a proposal, where: - The goals are defined. - The project is divided into phases. - Verifiers are defined for the end of each phase. - The requirements and initial ADRs are clearly stated. - The methodology to document and address bugs is defined - Etc. Before I give the proposal to Claude Code and start building, I always give the proposal to ChatGPT and Gemini, and they almost always find some potential for improvement. For me, it's very clear that reviewing a proposal by an LLM from a provider that is different from the LLM that created the proposal and/or will build the project is very beneficial. However, I don't know anyone else who does it. Am I the only one working like this or is it standard practice? Thanks! submitted by /u/AdBest7581 [link] [comments]

Best Model/Effort for Writing/RPG?

So, I use Claude for writing stories/RPG games. It's usually interactive games, of which the AI's capabilities are used for creating scenarios, describing actions, characters, everything one would expect a Master to do, but It's Claude. Since this latest update, that allows Claude 4.6 to work on 'Low, Medium, High and Max' effort, with the option of Adaptative Thinking, I noticed that while on normal use, my limits would be over by 30m-1h before the next cycle, now it ends 2h-2h30m before. Which means... more usage. I have been using it on Low effort, no Adaptative Thinking (does it consume tokens when activated? I think so), but still... I used to use Sonnet 4.5 for that, but it has been discontinued, which is a shame, because 4.5 was much better for storywriting than 4.6, but... whatever. So, do you guys have any tips for that? I have been using that tactic of copying the entire chat when it reaches a certain point (for me, it's usually between 3K-5K lines, which is right before it triggers the chat compression to free space), send it to Gemini or ChatGPT for consolidating and making a considerably shorter version of it with all I need (which tends to generate a document with up to 300 lines), paste that document in a new Claude chat and keep on from there. Another thing that I have been doing more often is to integrate these chats into a Project. So apparently it has shared documents and memories (does it? I'm new to that, sorry, I don't understand many concepts) which apparently makes it easier to continue these stories. I'm overextending myself here, but I just want to know what options do I have to make the usage less and enjoy Claude more. I use the ProPlan, because my computer has absolutely no way of running it locally. For the kind of thing I do, I need: consistence (because I divide my game in Episodes and Turns, the text must follow an specific structure of which the AI must always follow - 4.6 struggles with that from time to time, 4.5 used to handle that much better), creativity (after all it's an RPG game), memory (because that's a MUST!). Thanks for your help, sorry for the long text. Here's a TL;DR: Claude 4.6 Sonnet consuming too many tokens after EFFORT/Adaptative Thinking update. Using it for long storywriting and RPG. Can't run it locally (low spec PC). How to consume less? submitted by /u/Medium_Speaker3030 [link] [comments]

Anyone tried using AI models to screen candidates?

I used these two prompts on all AI apps to figure out who to vote for in the CA primaries: If you were running for governor of California, what will your big policies be ⁠Out of the candidates that are running in June election, who aligns closest to those policies Gemini, claude, chatgpt all ranked Matt Mahan (Democrat) as #1 Grok chose Steve Hilton (Republican) thoughts on AI use for voting decisions? submitted by /u/No_Mall_7299 [link] [comments]