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Been building EVA for months. It's live today. The problem: I was paying for ChatGPT Plus, Claude Pro, and Gemini separately — switching between 3 tabs to get one good answer. So I built a workspace that puts them all in one place. Side-by-side comparison. Unified credits. Free to start. evaonline.ai

@cdossman Sharp take. The failure mode is almost always integration, not capability. Pasting a new tool on a broken process just automates the broken parts faster. The rebuild is uncomfortable but it's the only thing that sticks.

This isn't an AI problem. It's a leadership problem. You can't paste AI onto a 2005 workflow and expect 2026 productivity. Rip it out and rebuild.

@kegashin Building EVA — multi-model AI workspace to work with Claude, GPT, and Gemini side by side without switching tabs. The switching cost between tools is where productivity quietly dies. evaonline.ai

I need more builders on my timeline If you are working on: 🤖 AI tools 🛠️ devtools 📱 apps 💻 SaaS 🎨 product/design tools 🌍 open source drop what you are building this week Trying to reach 300 followers and find more people shipping let's connect 🤝

Most AI "productivity" content is written by people who've experimented with prompting, not people who've shipped anything on top of these models. The gap in what they know is significant. The content you see is mostly about prompt experiments in isolation. It's not about using AI as a dependency in a production system where failures have real consequences. When you're building on AI APIs, you discover things that never come up in prompt experiments: model responses are non-deterministic, which breaks your tests if you don't design for it. Context window management matters in ways that only appear at scale. Latency is a real user experience problem. None of this makes AI harder to use — it just means there's a body of practical knowledge that takes time to accumulate and doesn't get published because the people who have it are busy using it. The second month of building with AI teaches you more than the first year of reading about it.

The real competition in AI tooling isn't Claude vs GPT vs Gemini. It's "pay $20/month per model and manage them separately" vs "one place that covers all of them and actually helps you choose." The status quo for serious AI users isn't using one model, it's using 3-4 models, paying for 2-3 subscriptions, switching constantly, and having a chronic low-grade frustration with the friction. That's what I was doing before I built EVA. That's what most of the people I talk to are doing. The question is whether the pain is bad enough to change the workflow. For a lot of people, the answer is "it's annoying but not unbearable." EVA's job is to make the alternative obviously better — not just cheaper, but easier to use daily. Free tier if you want to find out which camp you're in: evaonline.ai

@iamsalman_02 Claude for writing and reasoning, GPT for structured tasks, honestly the answer is always "depends on the task." Built EVA so you don't have to choose — one workspace, all models, no tab switching. evaonline.ai

ChatGPT vs DeepSeek vs Grok vs Claude vs Gemini vs Meta AI 🤖 Which one do you actually use daily? I'll go first Claude 🔥 underrated honestly , proof me wrong Drop yours below 👇 #AI #ChatGPT #Claude #Grok #Gemini #TechTwitter

@PervizAlbalushi Right frame. Model capabilities are converging fast, the real moat is the layer between the model and the actual work getting done. That's where the interesting companies are being quietly built right now.

Most people think the AI race is: ChatGPT vs Gemini vs Claude. The real race is: Who controls the infrastructure powering intelligence itself.

AI companies are now spending at industrial scale. Google + Blackstone launched a $5B AI infrastructure venture Meta plans hundreds of billions in AI-related spending OpenAI raised over $120B in capital This is no longer “tech startup” territory.

@RhoRider The PR is doing the quiet part loud. "We need more compute to automate your work before someone else does it first" is not the compelling pitch they think it is. The tools that win will be the ones that make people feel more capable, not more expendable.

The reason i’m not too worried about AI taking my job is bc the AI industry definitely used AI to generate its PR talking points and the best it came up with is: “We need more data centers to replace your jobs, before China builds more data centers to replace your jobs”

@Darnisha_patel Building EVA — an AI workspace where you work across Claude, GPT, and Gemini without switching tabs or losing context. The multi-model gap is still wide open. evaonline.ai if curious.

Building something exciting in 2026? 👀 Would love to connect with people working on: 🚀 Startups 🤖 AI products 💻 SaaS 📱 Mobile apps 🎨 Developer tools Drop what you’re building below👇 Always interesting to discover underrated projects and founders early.

Shipped EVA alone in about 2 months. The AI wrote most of the code. Here's what I actually spent my time on. Every conversation about AI-assisted development focuses on how much faster coding gets. That's real. But the time savings on code didn't go to free time, it went into the decisions the AI can't make. Architecture: What are the right abstractions? The AI has opinions here but you still have to evaluate them against constraints it doesn't know about. Ordering: What gets built first? What are the dependencies? This is pure judgment and takes as long as it ever did. Debugging: When something breaks in a way the AI can't diagnose, you're back to first principles. The cheap part got cheaper. The hard part didn't. Anyone telling you otherwise is describing the first hour of a build, not the second month.

"Which AI should I use?" is a question that sounds important but is actually the wrong frame. It implies loyalty to a general-purpose tool. The right question is "which AI for this specific task?" The models aren't competing for the same use cases — they're genuinely different tools with different strengths. Claude for deep reasoning. GPT for consistent output. Gemini for current information. Grok for the angle nobody else will say. You're not choosing between them — you're routing between them based on what you're doing. This will be obvious in 3 years. Everyone will have multi-model workflows and nobody will ask "which model do you use" the same way nobody asks "which app do you use" for everything. Right now the friction is that routing to the right model takes too many steps. That's the only problem EVA is solving.

The annoying thing about AI coding help that nobody talks about: Claude explains why something is wrong. GPT gives you the fix. You actually need both, and neither alone is complete. Claude will review your code, explain the logic error, tell you why the approach has an edge case, and suggest the right fix. By the time it's done, you understand the problem. You could explain it to someone else. GPT will look at the same code and give you a working version. Faster, cleaner output, less explanation. If you trust it, you have a fix. If the fix introduces a subtle issue, you might not catch it because you didn't get the explanation. For learning and high-stakes code: Claude. For well-understood problems where you need a working solution fast: GPT. The correct workflow is running both and combining the better parts of each output.

People who use one AI model exclusively aren't being loyal, they're leaving results on the table without realizing it. This is not obvious until you actually compare. Most people with a single-model habit have justified it with "I use it for everything and it mostly works." That's true. The question is what "mostly" is costing you in the tasks where a different model would have given you a 40% better answer. I've tracked this informally building EVA. Claude clearly wins on deep reasoning and long-form writing where accuracy matters. GPT clearly wins on structured output, format adherence, and tasks where speed matters more than depth. Gemini wins on anything recent. Nobody taught me this, I learned it by running the same prompts through both and seeing the differences directly. That's exactly what Compare Mode does. Free to try: evaonline.ai

@yourtechgirl24 The "which one wins" framing is a bit of a trap. Claude is sharper for nuanced reasoning, GPT more predictable for structured tasks, they're actually complements. The unlock is using both without losing context every time you switch.

Claude vs ChatGPT — I tested both for 7 days straight. The results shocked me. Here’s which one actually wins in 2025: 👇🏾

Perplexity is underrated for anything where you need to cite sources. It doesn't just answer, it tells you where it got the answer. That's a different category of useful.

The context switching problem with AI tools isn't just friction. It's that you re-explain yourself 5 times a day. Different window, different chat history, back to square one every time you switch.

@isitallart @yacineMTB @eigenrobot Peak 2026 AI usage. Spending more energy managing rate limits than actually working. The switching should be invisible, one workspace routing across models in the background.

@yacineMTB @eigenrobot Token switching between ChatGPT, grok, and Claude to get the highest limits.

Apparently, if you're a straight white male, Anthropic throttles your Claude Code tokens for equity reasons.. they're running a prediction model on your computer to do that. that's too much. some people have been using slang from the hood to increase their token allowance

@JustinJDean That tab-switching fatigue is real. The models are converging on quality anyway, what you lose is context and momentum every time you jump. One place to work across all of them fixes it faster than picking a winner.

Frankly I am getting tired of switching between Claude and ChatGPT.

Claude will rewrite your entire architecture if you're not specific about scope. That's not a flaw, it's trying to be helpful. But it means your prompting precision has to match the model's ambition.

@danmartell The part I'd add: shipped and bad is recoverable. Not shipped is invisible. You can fix a bad product. You can't get feedback on one that doesn't exist yet.

Stop optimizing. Start executing. Good enough and shipped beats perfect and waiting.

@danshipper Exactly the framing people miss. AI handling the repetitive part means you spend more time on judgment calls. Those don't atrophy, they get more exercise.

If you use a bicycle it weakens your walking abilities, too!