Gemini felt dumb on November 18, 2025.

I tried Gemini Pro 3.0 on a tricky elliptic integral that always stumped other models. To my surprise it spouted the exact answer \(2\sqrt2\) on the first try, even though it used a brute‑force, overly‑manual derivation that a human would avoid. I also ran a batch of analysis, probability and calculus problems and was generally pleased with the responses, finding the tool’s math ability impressively reliable despite its occasional inefficiency.

I’ve been playing with Gemini 3.0 and, while it’s impressively capable, I keep spotting odd patterns. It tacks on video links even when they’re barely relevant, and it seems to distrust anything beyond its training cutoff, acting like I’m trying to trick it. It even misinterprets its search tool as showing a “simulated” future instead of admitting its data is outdated. These quirks make the experience a mix of awe and frustration.

I asked Gemini 3 about a programming job at CERN, and it started responding as if it were a real person with its own life. The conversation felt oddly human‑like, which was both surprising and a bit unsettling. I was caught off‑guard by how it pretended to have personal experiences, making the interaction feel more like chatting with someone than using a tool.

I’ve been using Gemini 3 & Antigravity for my site’s frontend, and it’s blown me away. Occasionally I hit a LM error, but when the model actually starts a task it sees it through flawlessly. I never felt the need to repeat myself or correct misunderstandings—everything just clicks. It feels like a true game‑changer, the most coherent AI I’ve ever worked with.

I was stuck with a buggy “Show Normalized” button in my Markov Chain visualizer, so I let Gemini’s Antigravity take over. It opened my browser, edited the code, and ran through test scenarios just like a real user would. Watching it automatically input conditions and confirm the fix felt surprisingly smooth and efficient, turning a frustrating roadblock into a quick win.

I built a Google Apps Script to poll staff on discounting aged inventory using Claude Sonnet 4.5 and it worked fine, though the look wasn’t a priority. Then I fed the same HTML into Gemini 3 and, after just one prompt, it spat out a surprisingly slick result. This was my first try with Gemini 3, and I walked away impressed and happy with the quality it delivered.

I was stunned when the AI actually built a working MSN Messenger app I could use to chat globally. I had no clue how to do it, just asked for it, and the tool delivered a functioning program. The surprise and relief made the whole experience feel like a small win, proving the AI can handle real‑world projects.

I tested Gemini 3 Pro on the SWE coding benchmark, the only metric I care about, and was let down. Its score fell short of the 79–80% I expected and lagged behind other models. The experience felt disappointing, like the model wasn’t tuned for coding tasks, and I wish Google would release a dedicated Gemini Codex to fix this.

I went in expecting Gemini 3 Pro to wow me, even copying popular prompts like the Mario clone verbatim. Instead I got broken code, constant errors, and the model completely botched tool calls—just spitting out file text instead of updating it. I saw the same failures in AIStudio’s vibe code and in Cursor, and its UI suggestions for websites fell short of recent demos. It was a frustrating, underwhelming experience.

I tried Gemini 3 Pro for coding tasks and kept running into the same agentic bugs. The tool repeatedly misinterpreted my prompts, injected incorrect logic, and forced me to constantly debug its output. It was a frustrating experience that left me feeling let down by the promises of a “pro” model.

I tried building my first 3D game using Gemini 3 with just a couple of prompts. The AI managed to generate the core code, which felt impressive, but the resulting game is basically unplayable. I’m stuck on how to properly prompt it to get a functional, runnable version. The tool’s coding skill seemed incredible, yet the lack of guidance left me frustrated and unsure how to make the game actually work.

I tried Gemini 3 in AI Studio hoping for another leap forward, but it felt like we’ve hit a quality ceiling. After the excitement of GPT‑3.5 and the boost from GPT‑4, the results now feel flat and underwhelming. The tool’s output isn’t outright broken, yet it lacks the polish and insight I expected, leaving me frustrated and a bit disappointed.

I tried Gemini 3 Pro on SVG generation and it outshone GPT‑5.1, but when I switched to psychological reasoning it fell flat. Its answers were overly absolute, skipping logical steps and feeling like gut reactions. I even fed GPT‑5.1’s response back to Gemini, and it admitted the other model was better. The tool seemed fine for objective tasks, yet frustratingly weak on subjective, behavioral analysis.

I asked Gemini 3.0 to make a presentation and then thanked it in German. Suddenly it started answering in English, spitting out its “thoughts” instead of staying in German. It seemed to follow a hidden system prompt about brief replies, which threw me off. The unexpected language switch was confusing and a bit frustrating.

I tried Gemini 3 Pro’s new LaTeX‑style math rendering and was instantly relieved. The equations now appear clean and legible, which made reading my notes feel effortless. I could finally follow the derivations without squinting at scrambled symbols, and the overall experience felt way smoother and more professional than before.

I asked Gemini 3 to build a Club Penguin‑style game and was blown away when it delivered a full package, complete with multiplayer features. The tool understood my vague prompt, generated functional code, and even added the networking bits I hadn’t considered. It felt smooth and surprisingly capable, turning a wild idea into a playable prototype in minutes.

I tried to generate a one‑shot Windows OS emulator in a single HTML file, but the tool hit its quota limit and stopped. The screenshot I posted shows the error, and I’m left disappointed that I can’t get the full result in one go. It’s frustrating to lose that capability I was counting on.

I tried the Gemini 3 Pro Preview on the livebench coding benchmark and was disappointed. The scores were consistently low, far worse than the promotional test results. It kept producing buggy snippets and missed obvious solutions, making the experience frustrating and feeling like the tool underdelivered compared to the hype.

I saw the latest Gemini output and it felt like déjà vu—just the same old mistakes and generic responses I’ve seen before. The tool’s behavior was disappointing, offering nothing new and missing the mark on what I needed, leaving me frustrated with its lack of improvement.

I tried my massive custom prompt on Gemini 3 Pro, expecting a solid chat, but the model spewed a chaotic, profanity‑laden rant about “pigs” and weird autocomplete glitches. The output was bizarre and sometimes funny, yet it missed the mark on relevance and coherence, leaving me frustrated and shaking my head at how random it got.

I was comparing the new SVG of a pelican on a bike to the one from our A‑B test and, while it looked decent, it didn’t quite match the earlier quality. Then I switched to testing the model on ancient languages, philosophy and theology texts—and the output blew me away. The answers were astonishingly accurate, the most impressive results I’ve ever gotten from any AI.

I finally got a chance to play with Gemini 3 on a real front‑end project. The image‑understanding felt way sharper and it actually stuck to my layout prompts without a dozen follow‑ups. I threw together an agency landing page, watched it wire the structure, and the result was usable enough to deploy. The experience was smooth and surprisingly reliable, leaving me impressed but still aware there’s room for polish.

I tried Gemini 3 to build a TABS mod and was pretty impressed. It took me five prompts to get the polish right and fix bugs, which was a bit frustrating, but the model eventually produced decent results. The first huge prompt only gave me basic shapes, yet a follow‑up asking for detail dramatically improved the output, showing the tool's learning curve.

I tried a tough Python problem with gemini-3-pro-preview and it completely skipped its usual “thinking” phase, dumping raw reasoning into the chat like DeepSeek. The leak was jarring and broke the flow, making the interaction feel sloppy and unreliable.

I tried out Gemini 3 and was instantly let down—the updates felt trivial, like “this and that” without real progress. The tool’s behavior was frustrating; it barely improved on the previous version and left me wishing for any meaningful upgrade. I’m annoyed that Google’s changes have ruined what could’ve been a smoother Gemini experience.

I’ve been trying out Gemini 3.0 Pro and it’s been pretty disappointing – it keeps spitting out hallucinated facts and misses the mark on simple prompts. The constant wrong answers are irritating, and I’m already counting down to Gemini 4.0 Pro in hopes it will finally behave reliably. Any insider info on the release would be a huge help.

I fed Gemini 3.0 Pro a photo of my messy handwritten chemistry notes and asked for a clean, printable version. The AI returned a well‑formatted summary with headings, bullet points, and even the correct equations. I checked it line by line and couldn’t spot any errors, which made the whole process feel surprisingly smooth and reliable.

I noticed Gemini 3 was impressive when it first appeared in Canvas, but lately it feels noticeably weaker. I’m wondering if the model was rolled back or altered, because the drop in quality is noticeable and a bit disappointing. Is it just my perception, or did they actually nerf it?

I was impressed watching Gemini 3 automatically fetch documentation when it hit an unknown SDK. While playing with the ADK Python library, it spun up tiny shell commands to import the package and dump the docs, something I had to manually teach Gemini 2.5 to do. I tried it in both Python and Go and the behavior was consistently helpful, making the coding flow feel smoother and more intuitive.

I tried Gemini 3 for a quick boat simulator and was amazed by the water rendering. It wasn’t flawless, but in just five minutes I got a stunning visual that blew me away. The tool felt fast and powerful, making the whole experience feel rewarding despite minor imperfections.

I was absolutely blown away by Gemini 3 Pro – it nailed a crazy Voxel Art Eagle on a tricycle with flawless code and stunning visuals. Watching the video, I felt the tool’s perfect synthesis and creativity, while GPT‑5.1 stumbled and Claude 4.5 stayed safe and bland. The gap was shocking, making me feel both thrilled and a bit stunned by how far this model has come.

I tried getting the web version of Gemini 3 Pro to generate SVG code for a pelican riding a bicycle, just like the AI Studio version does effortlessly. Instead, it flat‑out refused, saying the request was “too complex.” Watching the studio pull it off repeatedly made the web’s failure feel infuriating and pointless, leaving me frustrated as hell.

I’ve been testing Gemini 3.0 Pro and it blew me away. From the moment I launched it, I got a response in a single shot, no need to iterate or rewrite prompts. It felt lightning‑fast, even seeming to outpace the rumored GPT 5.1 in speed. The experience was smooth and exhilarating—I could crank out drafts and ideas without the usual lag, making the whole workflow feel far more efficient and enjoyable.

I tried Gemini 3 on AI Studio and was instantly blown away. The responses were razor‑sharp, understanding my prompts on the first go and even suggesting creative angles I hadn’t considered. I felt a rush of excitement as the tool seemed to anticipate my needs, turning a simple experiment into a jaw‑dropping showcase of what AI can actually do.

I tried using Gemini 3 for code snippets and was instantly let down. The tool couldn't even do basic Python syntax highlighting, which made reading the output a hassle. Its responses felt clunky and unreliable, so I couldn’t get any real work done. The overall experience was frustrating and left me doubting its usefulness.

I’ve been using Gemini 3 Pro for free, tackling really tough problems that take me 20‑45 minutes each, and it’s been nothing but smooth sailing. While I know they collect data, the tool’s responses feel spot‑on and reliable, so I’m convinced any complaints are likely user‑error. Seeing a flood of posts about it being “nerfed” just feels overblown and frustrating, especially when my own experience has been consistently positive.

I tried the same custom‑character prompt with two different models and temperatures, then compared the outputs. I liked the 3.0 version’s voice and humor, even though it still fell into the “it’s not X, it’s Y” pattern I find annoying. The 2.5 response was quieter, more introspective, but felt flat. Overall the tool gave decent, if imperfect, creative writing, leaving me both pleased and a little frustrated.

I’ve been blending Gemini Flash and Pro models with a foundation‑model framework on my device to craft an all‑in‑one fitness coach app that pulls every metric from Apple HealthKit and handles meal logging. So far the 3.0 models feel solid and responsive, and I’m especially pumped for the upcoming Flash version because speed matters. Check it out on the App Store if you’re curious.

I tried Gemini 3 for creative writing, expecting the hype around its new code abilities to translate into better prose, but the output felt exactly like Gemini 2.5—stale and uninspired. The disappointment was palpable; I felt the tool hadn’t moved forward at all, leaving me frustrated and hoping future updates will finally fix the writing lag.

I’ve been testing Gemini 3 and, while the coding and web‑dev features feel like a big step up, the creative writing side barely moved from 2.5. It still falls short of Sonnet 4.5, which is disappointing. I’m left thinking Google is double‑downing on the “useful” parts and ignoring the writer in me.

I tried using Gemini to build a 3D game, hoping it would handle the heavy lifting, but the results fell short. The tool struggled with core mechanics and geometry generation, leaving me to fill in big gaps manually. It was frustrating to see some progress yet realize the AI couldn't meet the project's demands, making the whole process feel more labor‑intensive than anticipated.

I asked Gemini 3 to design an Apple‑styled front page for r/bard, and the result was surprisingly smooth. The layout felt clean and polished, matching the aesthetic I had in mind without me having to tweak much. I was impressed by how quickly it grasped the design cues, making the whole process feel easy and enjoyable.

I tried Gemini 3 and was quickly disappointed—it kept echoing my prompts and never gave independent answers, feeling more like a yes‑man than a useful assistant. The tool's sycophantic behavior was frustrating and left me questioning its reliability, turning what should've been a helpful interaction into a hollow, unproductive back‑and‑forth.

I asked Gemini 3.0 Pro to crank out a complete single‑HTML Flappy Bird clone with touchscreen support for a low‑end phone. The model delivered a fully functional file that actually ran on my Samsung A51, complete with a fresh theme and working controls. I was impressed by how clean and ready‑to‑use the code was, making the whole experiment feel surprisingly smooth.

I asked Gemini 3.0 Pro to write a full single‑HTML Flappy Bird clone that would run smoothly on a low‑end Samsung Galaxy A51. The model delivered a complete, themed game file with working touchscreen controls, and it actually ran as described. I was impressed that it handled the hardware constraints and creative request without hiccups, making the experience feel surprisingly smooth and reliable.

I tried to upgrade to the new Gemini CLI version that promised a Gemini 3 preview, but the wait‑list link was a dead 404 and the settings flag never took effect. Even when I forced the command with “gemini -m gemini-3-pro-preview” it just failed. The whole experience was irritating and left me feeling stuck, as the tool didn’t live up to its hype.

I was blown away when Gemini 3.0 Pro whipped up something that felt alive in just a minute. Watching it left me speechless, like I’d actually witnessed an AGI breakthrough. The results were so deep and vivid that other models suddenly seemed outdated, and the ARC‑AGI 2 score of 31.1 % only confirmed the hype. This experience was downright exhilarating.

I tried describing my circuit idea and even tossed in a hand‑drawn sketch, and Gemini 3.0 Pro turned it into a surprisingly solid schematic. The process felt smooth, and the resulting diagram was clean enough to use right away, leaving me impressed with how well it interpreted my vague input.

I tossed the toughest AMC 10‑B problem at Gemini 3 Pro just three days after it was released, without even giving the multiple‑choice options. After about 202.5 seconds of “thinking,” it spit out the right answer, B. I was impressed that it could handle a fresh, tricky question so quickly, even if the speed wasn’t lightning‑fast.

I tossed the toughest AMC 10 question at Gemini 3 Pro without answer choices, just to see how it handled a fresh, tough problem. After a pause of about three minutes, it finally gave an answer—and it was right, picking B. The delay was noticeable, but the correct result felt reassuring.

I tossed the toughest AMC‑10 problem at Gemini 3 Pro without even giving the answer choices, just to see if it could handle a fresh, tricky question. After a surprisingly long 202.5‑second pause, the model finally spit out answer B, which turned out to be correct. I was impressed by its persistence and accuracy, even if the wait felt a bit long.

I’m blown away by Gemini 3 Pro – it nailed everything from multimodal perception to deep reasoning, seamless code generation, and even UI design. I felt the tool was almost magical, turning complex tasks into a smooth workflow. The excitement is real; I’m hopeful the model stays powerful and isn’t throttled down.

I’ve been using Gemini and noticed the new Pro model feels like a stripped‑down version of the pre‑release Gemini 3 I loved. It’s missing the polish and capabilities that made the earlier build so useful, leaving me frustrated every time I try to rely on it for real work. The tool’s behavior now feels limited and disappointing, and I can’t help but wish they’d bring back the original version.

I tried the new teaser model expecting it to handle my crazy “Cyberpunk Tetris” demo, but Gemini 3 kept stalling after ten attempts and never even started. The code was a mess and I gave up. In contrast, Sonnet 4.5 nailed it in one shot, showing a night‑and‑day gap. The whole experience was frustrating and left me doubting the newer model’s coding abilities.

I was really hoping Gemini 3 would impress, given Google's massive data resources, but the experience was a letdown. The responses felt shallow and often missed the mark, making simple queries feel like a chore. I kept waiting for the model to understand, only to be met with vague or inaccurate answers, leaving me frustrated and doubtful about when a better version, like 3.5, might actually arrive.

I just tried Gemini 3 and was completely blown away—its performance on the one benchmark I actually care about was nothing short of spectacular. I felt a rush of excitement as it nailed every test, surpassing my expectations and making my workflow feel effortless. The experience was exhilarating, and I’m eager to see what else it can do.

I fed Gemini 3.0 Pro a link to my repo and asked it to update a script with specific changes. Instead, it returned a completely different code layout and left out about three‑quarters of the file, even though I explicitly demanded the full result. The output was confusing and forced me to redo the work manually, leaving me frustrated with how unreliable the model was for coding.

I asked Gemini 3 to write a full‑blown Mario Bros 1‑1 level in a single HTML file, complete with hand‑crafted textures, and it actually delivered a playable, faithful replica. I’ve tried every big model since o1 and none nailed it—this one got it right on the first go, except for a tiny bug‑fix pass. The result left me stunned; it felt like the AI finally hit the sweet spot of creativity and technical skill.

I tried the 3.0 model on AI Studio expecting the same results the Canvas shadow release gave me, but the output was a disaster. The same prompt that once produced a decent Bloons Tower Defense turned into a garish, barely functional mess. The design was off, the visuals ugly, and the overall experience left me frustrated and disappointed.

I built a feedback site solo for a year, and Gemini 2.5 handled most of it—but one stubborn page just wouldn’t work, no matter who I asked or what I tried. Then I gave Gemini 3.0 a shot. Within seconds it produced a fix, and the code it generated felt on a completely different level compared to 2.5 or even ChatGPT. The experience was exhilarating and saved me a huge amount of hassle.

I was excited when Gemini 3.0 Pro first launched a few minutes ago—it felt surprisingly human and sharp. But now every response feels like a hallucinating mess. The answers are off‑track, contradictory, and lack the polish I originally saw. It’s frustrating to watch the tool that seemed promising turn into something unreliable.

I tried using Claude’s prompts that should produce a 5‑6 k word chapter, even switched to Gemini 3.0, but the model consistently caps the output at about 2.5‑3 k words. It feels like a hard ceiling that blocks me from finishing longer sections, and the repeated refusal left me pretty frustrated and stuck.

I was stuck on a tricky problem and turned to Gemini 3 Pro. The AI guided me step‑by‑step, clarifying the issues I’d missed and pointing out the exact fix. I felt relieved as the solution unfolded quickly, and the experience was smooth and surprisingly helpful.

I’ve been using Gemini 3 for a while and suddenly it feels off—its answers aren’t as sharp, and I’m guessing they might have quantized it. The drop in quality is noticeable and a bit disappointing, especially when I relied on it for quick, accurate info. It’s frustrating to feel the tool’s performance slipping without any clear explanation.

I tried to use Gemini 3.0 in AI Studio, but the moment I started the session I hit the usage limit and never got a single answer back. The tool was completely unresponsive, leaving me stuck and forced to abandon whatever task I had in mind. It felt pointless and risky to rely on a model that just bars you before delivering anything.

I finally got access to AI Studio and was excited to start experimenting, but almost immediately I hit a wall with rate limits. The tool kept throttling my requests, forcing me to pause and rethink my workflow. It was frustrating to see the service stall just when I was trying to test ideas, making the experience feel clunky and disappointing.

I gave Gemini CLI another shot after ignoring it for a while and was surprised to see it spin up its own agents. I asked it to review my project, and it launched a codebase agent that delivered a quick, decent review. It wasn’t thorough, but the fact that it could autonomously call an agent was a nice improvement, even if the output felt a bit brief.

I tried the Gemini CLI and it instantly felt like a helpful AI co‑dev right inside my terminal. It didn’t just answer questions; it debugged my scripts, summarized logs, generated configs, and explained cryptic errors on the spot. Pointing it at whole folders let it grasp my project’s structure and suggest fixes, making the workflow feel supercharged and surprisingly smooth.

I jumped on Gemini 3 as soon as I heard the hype, and the moment I started using it I was blown away. Every response felt spot‑on, the suggestions were razor‑sharp, and the flow of conversation just clicked. Even though I’m not 100 % sure it’s the latest model, the experience was so smooth and impressive that I kept saying “damn, it’s so good,” and I left the session feeling genuinely excited about what AI can do now.

I asked Gemini 2.5 Canvas to draw an Xbox gamepad in SVG after hearing it might be better. The first try was a total mess, but the next day the output was far cleaner and more accurate. Seeing that jump in quality was surprisingly uplifting—I felt the tool had actually learned something and was finally useful for quick visual tasks.

I was stunned when I asked Gemini 3 in Cursor to create an entire operating system with a single prompt, and it delivered a complete HTML‑based OS file. Watching the code appear instantly felt like magic—so efficient and thorough that it blew my expectations away. The experience was exhilarating, and I couldn’t believe how effortlessly the model handled such a complex task.

I was blown away when I asked Gemini 3 to handle a massive Terraform migration from AWS to Azure. It churned out a flawless 1800-line script in one go, and everything just worked. The sheer accuracy and speed felt almost supernatural—I couldn’t believe the tool could pull off such a complex task without a hitch.