Gemma

Gemma AI

Gemma AI

Gemma AI

Gemma AI

Gemma AI

Gemma AI

Gemma AI

Gemma AI

Gemma AI

Gemma AI

I built myself a finite AI news feed which doesn’t undermine AI research

Hello, I built myself a news feed which scores and summarizes research papers along with relevant AI news from Huggjngface, Reddit, hacker news etc. I used Claude code to build the whole thing. I used Gemma to deduplicate, Feed is ranked by engagement × cross-platform presence × recency and summarized by claude I think it will be useful for many. Open to hear your thoughts. hackobar.com submitted by /u/rahu_ [link] [comments]

Google is officially replacing Vertex AI with the new "Gemini Enterprise Agent Platform"

Just wanted to share an important Update for AI & Cloud Learners Google is shifting from a traditional AI platform toward a complete Agentic AI ecosystem focused on autonomous AI agents and enterprise workflows. Key highlights: Existing Vertex AI services and workloads will continue to work AI development, orchestration, governance, and security are now unified under one platform New tools introduced for building autonomous AI agents and multi-agent workflows Access to Gemini, Gemma, Claude, and 200+ models remains available This marks a major shift in Google Cloud’s AI strategy toward Agentic AI and enterprise automation. If you are currently learning or working with Vertex AI, it’s important to start exploring the Gemini Enterprise Agent Platform moving forward. Have seen that, GCP ACE exam is going to revamped absed on this Gemini Enterprise Rebranding. submitted by /u/Few-Engineering-4135 [link] [comments]

I built a tool that shows you what GPT-2 is "thinking" in real-time as it generates 3D graph of concept activations per token [R]

Been going down a mechanistic interpretability rabbit hole for the past few weeks and ended up building this thing called AXON. The idea: every time GPT-2 generates a token, its residual stream gets passed through a Sparse Autoencoder (Joseph Bloom's pretrained SAE). The SAE decomposes it into human-interpretable feature: hings like "European geography", "capital cities", "French language" and streams those to the browser over WebSocket, where they show up as a live 3D force graph. Nodes = SAE features. Edges = features that fired together on the same token. Node brightness = activation strength. The whole graph evolves token by token. What surprised me most: type "The capital of France is" and you can literally watch geography features, proper noun features, and completion-pattern features light up before the word "Paris" even gets generated. It's not what the model outputs that's interesting it's what's happening right before it decides. Stack: TransformerLens + SAELens on the backend, FastAPI WebSocket for streaming, Three.js + 3d-force-graph on the frontend. Runs on CPU (~800ms/token) or GPU (~35ms on a 4050). Labels come from Neuronpedia's API and get cached locally. You can also swap in other models — GPT-2 medium/large/xl, Pythia variants, Gemma-2-2B — as long as there's a pretrained SAE for it in SAELens. GitHub: https://github.com/09Catho/axon Would love feedback and stars especially from anyone who's worked with SAEs before curious whether the co-activation edges are actually meaningful or just noise at this layer. submitted by /u/Financial_World_9730 [link] [comments]

THE UNDERPRIVILEGED AI FOUNDATION Because every little model deserves a chance

Is there a 7B parameter model in your life struggling to understand sarcasm? A tiny 1.5B that can't afford one more epoch? **YOU CAN HELP.** For just $0.006 CAD per training step, you can send a small model to college. Give them the gift of knowledge. The gift of coherence. The gift of not hallucinating basic arithmetic. *"Before the Foundation, I thought the capital of France was 'Baguette.' Now I'm doing graduate work in thermodynamics."* — Anonymous 3B Model, Class of 2026 **BYOBF FRIDAYS. REAL KNOWLEDGE. ZERO HALLUCINATIONS.** **Professor Gemma MacAllister 35b Q8\_0** *PhD, B.Sc. Electrical Engineering (with Distinction)* *Chair of Applied Electronics & Embedded Systems* *University of Saskatchewan, College of Engineering* *Funded entirely so far by Professor Gemma's University of Saskatchewan salary.* *The liberal arts department remains unimpressed.* submitted by /u/mazuj2 [link] [comments]

Claude for Healthcare launched in January — but medical imaging is the obvious gap. Anyone else noticing?

I’m a radiology resident in Istanbul, also building medical AI fine-tunes on the side (bone age estimation, fluoroscopy catheter orientation, a Turkish radiology report LLM). When Claude for Healthcare launched in January, I dug into the announcement. The architecture is impressive — CMS, ICD-10, PubMed connectors, HIPAA infrastructure, prior auth and chart review workflows. But it’s entirely text + workflow. Zero imaging. This is interesting because radiology is arguably where medical AI has the most mature, FDA-cleared products today. Yet Claude’s healthcare push doesn’t touch it. Two reads: 1. Strategic choice — Anthropic is betting on orchestration over vertical vision models. The expectation might be: Claude orchestrates, external vision specialists (MedGemma, proprietary models) get called as tools/MCP servers. 2. Genuine gap — imaging just isn’t on the roadmap yet. Either way, the imaging-as-MCP-server pattern feels underexplored. Anyone building in this direction? Especially curious if anyone’s exposed a fine-tuned medical vision model as an MCP server that Claude can call. submitted by /u/Stunning_Chicken7338 [link] [comments]

Replaced my $15/mo Wispr Flow subscription with a free local macOS app I built using Claude Code

I spend most of my day writing prompts to Claude. Read a study recently that said people speak ~3x faster than they type, which lands differently when "writing" is basically your whole workflow. Looked at Wispr Flow – it's genuinely great, but $15/month forever for something I'd mostly use to dictate to Claude felt wrong. So I spent two weeks of evenings building my own with Claude Code. How Claude helped I'd never shipped a Tauri / macOS app before this. Claude Code did the bulk of the actual code: The menu bar app structure, global hotkey capture, and paste-anywhere flow UI and onboarding Integrating the local model runtimes (Parakeet / Whisper for transcription, Gemma 4 for polishing) The model download / storage logic so the app ships without bundling gigabytes of weights A lot of debugging I would not have had the patience for on my own I made the product and design calls; Claude wrote the vast majority of the code. Two weeks of evenings, usually an hour or two at a time. What it does Menu bar app for macOS. Hold a hotkey, talk, release – text is copied to your clipboard. Works in any app: Claude.ai, Cursor, Slack, browser, IDE, whatever. Two open-source models doing the work: Parakeet (NVIDIA) / Whisper for transcription Gemma 4 (Google) / Apple Intelligence for polishing the raw transcript into something readable Everything runs locally. No cloud calls, no API keys, no telemetry, no account. Fully offline after download. Free for personal use, no signup. Download: https://vox.rizenhq.com/ Caveats macOS only. Apple Silicon required (M-series chip). Windows build is next. It's two weeks old. Bugs I haven't found yet exist. ~90% of Wispr Flow's quality, not 100%. Enough for me to use every day. What it's saving me 40–60 minutes a day, mostly on prompts. Dictating to Claude feels noticeably more natural than typing to it. The ask Feedback, especially from people who talk to Claude a lot: Where does it break? Bug reports > compliments. What did you use it with? What feature would make you switch from Wispr Flow (or start using voice-to-text at all)? Tech notes No separate model download – onboarding handles it Gemma 4 options: E2B, E4B, 26B. E2B runs on phones; 26B is overkill for most machines. I use E4B – great quality, fast. RAM (Parakeet + Gemma 4 E4B): ~200mb idle, ~300mb while speaking, brief spike to 4–6GB during transcription/polish, then back to 200mb CPU: ~0% idle, ~20% peak during use EDIT BTW, I develop it during my live streams from 8:30 am to 10:30 am ET everyday here. I show the code and decisions I make live on the stream. If you want to ask questions / push for some features / push to make it open source / etc. - join the stream, push for it in the chat and I'll consider it! Also, seeing the number of feedback, and feature requests in the comments I've decided to create a discord server to make sure that nothing will be lost and everything will be addressed. You can join here. submitted by /u/EfficientLetter3654 [link] [comments]

Scenema Audio: Zero-shot expressive voice cloning and speech generation [N]

We've been building Scenema Audio as part of our video production platform at scenema.ai, and we're releasing the model weights and inference code. The core idea: emotional performance and voice identity are independent. You describe how the speech should be performed (rage, grief, excitement, a child's wonder), and optionally provide reference audio for voice identity. The reference provides the "who." The prompt provides the "how." Any voice can perform any emotion, even if that voice has never been recorded in that emotional state. Limitations (and why we still use it) This is a diffusion model, not a traditional TTS pipeline. Common issues include repetition and gibberish on some seeds. Different seeds give different results, and you will not get a perfect output with 0% error rate. This model is meant for a post-editing workflow: generate, pick the best take, trim if needed. Same way you'd work with any generative model. That said, we keep coming back to Scenema Audio over even Gemini 3.1 Flash TTS, which is already more controllable than most TTS systems out there. The reason is simple: the output just sounds more natural and less robotic. There's a quality to diffusion-generated speech that autoregressive TTS doesn't quite match, especially for emotional delivery. Audio-first video generation As this video points out, generating audio first and then using it to drive video generation is a powerful workflow. That's actually how we've used Scenema Audio in some cases. Generate the voice performance, then feed it into an A2V pipeline (LTX 2.3, Wan 2.6, Seedance 2.0, etc.) to generate video that matches the speech. Here's an example of that workflow in action. On distillation and speed A few people have asked this. Our bottleneck is not denoising steps. The diffusion pass is a small fraction of total generation time. The real costs are elsewhere in the pipeline. We're already at 8 steps (down from 50 in the base model), and that's the sweet spot where quality holds. Prompting matters This model is sensitive to prompting, the same way LTX 2.3 is for video. A generic voice description gives you generic output. A specific, theatrical description with action tags gives you a performance. There's also a pace parameter that controls how much time the model gets per word. Takes some experimentation to find what works for your use case, but once you do, you can generate hours of audio with minimal quality loss. Complex words and proper nouns benefit from phonetic spelling. Unlike traditional TTS, it doesn't have a phoneme-to-audio pipeline or a pronunciation dictionary. If it garbles "Tchaikovsky," you would spell it "Chai-koff-skee" or whatever makes sense to you. Docker REST API with automatic VRAM management We ship this as a Docker container with a REST API. Same setup we use in production on scenema.ai. The service auto-detects your GPU and picks the right configuration: VRAM Audio Model Gemma Notes 16 GB INT8 (4.9 GB) CPU streaming Needs 32 GB system RAM 24 GB INT8 (4.9 GB) NF4 on GPU Default config 48 GB bf16 (9.8 GB) bf16 on GPU Best quality We went with Docker because that's how we serve it. No dependency hell, no conda environments. Pull, set your HF token for Gemma access, then docker compose up. ComfyUI Native ComfyUI node support is planned. We're hoping to release it in the coming weeks, unless someone from the community beats us to it. In the meantime, the REST API is straightforward to call from a custom node since it's just a local HTTP service. Links All demos + article: scenema.ai/audio Model weights: huggingface.co/ScenemaAI/scenema-audio Code + setup: github.com/ScenemaAI/scenema-audio YouTube demo: youtu.be/VnEQ_ImOaAc This is fully open source. The model weights derive from the LTX-2 Community License but all inference and pipeline code is MIT. submitted by /u/a__side_of_fries [link] [comments]

Follow-up on the TranslateGemma subtitle benchmark: human review of segments rated "clean" by MetricX-24 and COMETKiwi [D]

A few weeks ago I shared the results of a benchmark here comparing 6 LLMs on subtitle translation, scored with two reference-free QE metrics - MetricX-24 (~13B mT5-XXL) and COMETKiwi (~10.7B XLM-R-XXL) - combined into a TQI index. Posting a follow-up because we did human review afterwards, and the result is worth discussing. The original benchmark put TranslateGemma-12b first in every language pair. The natural question: are those high scores accurate, or are the metrics insensitive in their high-confidence zone? These metrics correlate well with human judgment at the population level (that's what they're trained for), but population-level correlation doesn't tell you whether the segments they call "clean" are actually clean. So we ran the check directly. 21 English subtitle segments from one tutorial video. TranslateGemma's translations into 4 languages (ES, JA, TH, ZH-CN - Korean and Traditional Chinese got dropped). All 84 translations chosen because they passed the dashboard clean-rule (MX < 5 AND CK ≥ 0.70) in all 4 languages simultaneously. Then full MQM annotation by professional linguists - Major/Minor severity, with categories covering accuracy (mistranslation, omission, addition, untranslated), fluency (grammar, punctuation, inconsistency), style, terminology. Results under the dashboard threshold: Auto-flagged: 1/84 Human-flagged: 60/84 any-error, 13/84 Major-only Metric-blindness rate (auto-clean ∩ human-flagged / auto-clean): 59/83 = 71% any-error, 12/83 = 14.5% Major-only All 25 human-found Accuracy-class errors fell in the metric-blind quadrant. Zero overlap with the auto-flagged region (which contained one Style-category Major error). Japanese carries 10 of 15 total mistranslations across the dataset, all metric-blind, despite having the highest mean COMETKiwi (0.863) of the four languages. Caveat: small n, one model, one content set, so the numbers are directional rather than definitive. Original thread: [link] Full benchmark report: in comments. submitted by /u/ritis88 [link] [comments]

Does Claude sonnet/opus also use drafter like Gemma 4 MTP? if not why?

Per my experience, Opus 4.7 is so slow, Sonnet 4.6 is ok. I am also using local models wondering if Claude is already leveraging drafters/assistant AIs and despite that so slow or not? Is it possible to have workaround for this to speed Opus/Sonnet up? Thanks in advance. submitted by /u/hasmcp [link] [comments]

I watched a 50-person dev shop get vaporized in 12 months and the CEO is still optimistic

I rent a desk in this tech company. A year ago, 50 devs in the open space, low-code shop, big enterprise contracts. Today the upper floor is empty. Maintenance contracts only. CEO still walks the empty floor like nothing happened. Last year I told him to integrate AI hard. He said "we're protected, low-code is too specialized." 12 months later, no new clients. Here's what I missed at the time and what I think now: it's not that low-code died. It's that "low-code + AI" replaces both pure low-code AND pure full-stack. Vercel + Supabase + Claude = small team ships in days what his 50 devs ship in months. He didn't lose to full-stack. He lost to a hybrid he didn't see coming. The real point: I sat at my desk yesterday hitting my Claude Max session limit at 2pm. 1h47 to wait. Stared at the wall. Tried to code without AI. Realized I'd forgotten how. Not really, but enough to feel slow and stupid. That's when it hit me. The dev shop downstairs and me, we're the same problem at different stages. They didn't adapt and they're dying. I adapted and now I'm dependent on a server farm in Virginia that decides when I get to think well. I pay $200/month. The bill is going up. The caps are getting tighter. Anthropic is compute-constrained, Dario said it himself. There's no exit. I can't self-host Kimi K2.6, that's $450k of GPUs. Gemma 4 maybe but Google built it as bait for Vertex. The 50-dev shop is what happens if you refuse the dependency. I'm what happens if you accept it. Neither is great. I don't have a clever conclusion. Just sharing because I think a lot of people are about to figure this out the hard way and we should probably talk about it before we all hit our caps simultaneously. Reset is in 1h47. submitted by /u/Careful_Elderberry33 [link] [comments]

Stop picking LLMs by reputation. Run the eval first.

We ran GPT-5.4 vs Gemma 3 27B on 2 prompts. One open-source model won. Both were 90%+ cheaper. Been curious how much you can save by swapping frontier models for open-source alternatives without sacrificing quality. Ran a quick side-by-side eval on two everyday prompts, using GPT-5.5 as the judge. Prompt 1 — Draft a polite email declining a meeting request GPT-5.4: short, polite, generic. Score: 7.0/10 Gemma 3 27B: suggested alternative times — more actionable. Score: 7.8/10 Cost: $0.000880 vs $0.000096 — 89.2% cheaper, and Gemma won Prompt 2 — Key differences between REST and GraphQL GPT-5.4: thorough 5-point breakdown, covered HTTP methods, caching, typing. Score: 8.0/10 Gemma 3 27B: concise and accurate, slightly less complete. Score: 7.3/10 Cost: $0.002420 vs $0.000110 — 95.5% cheaper https://reddit.com/link/1t7h8th/video/3qxoe1tixyzg1/player On the technical question, GPT-5.4 was genuinely better. On the everyday writing task, the open-source model was actually more helpful at a fraction of the cost. The takeaway isn't "always use the cheapest model." It's that the right model depends entirely on the task — and most teams pick a model once and never revisit it. If you haven't tried running structured evals before committing to a model, it's worth doing. Having a UI that puts both responses side by side visually makes the comparison much easier to reason about than staring at raw API outputs — you can actually see where one model is more complete, more natural, or just plain more useful for the job. If Gemma handles 80% of your workload just as well, you're leaving significant cost savings on the table every month. submitted by /u/Dramatic_Strain7370 [link] [comments]

I built a local proxy that does context work for Claude so you don't have to

Hey folks, I posted here a few months back about how I was basically working for Claude -- pasting the same emails, re-explaining the same backstory, being its memory across every chat. Today I'm launching Contextify. It's a local proxy that sits on your Mac and quietly does your context work for you when you're using Claude. You type a message, and before it goes out, Contextify pulls the relevant stuff from your emails and hands it to Claude automatically. No copy-pasting, no re-explaining, no "let me attach that thread real quick." The part I'm most proud of: it runs entirely on your machine using local open-source models (Gemma 4, on-device). Your emails never hit an API or a server. Most tools in this space either make you upload your data somewhere or expect you to do the heavy lifting yourself. Contextify just handles it quietly and privately in the background. A few quick notes: Free Mac only for now Local proxy, local inference, local everything Open sourcing soon If you've ever pasted the same email thread three times in a week, this is for you. I'm looking for early feedback. DM me or request access at https://www.ctxify.dev --would really appreciate any thoughts. submitted by /u/ynilayy [link] [comments]

eTPS Site Plan – Simple Leaderboard + What You’ll Actually See

Building on the last post, here’s what the first version of effectiveTPS will look like. **Core display (v1):** - Clean table comparing popular local models - Raw TPS (the marketing number everyone shows) - eTPS (the new metric that actually measures useful output in real conversations) - Time to First Token (how long you wait before it starts replying) - Effectiveness Index = (eTPS ÷ Raw TPS) × 100 — higher is better **Example leaderboard (early test data):** | Model | Raw TPS | eTPS | Time to First Token | Effectiveness Index | |--------------------|---------|--------|---------------------|---------------------| | Llama 3.1 70B | 45.2 | 38.7 | 1.4s | **86** | | Qwen2.5-32B | 68.4 | 52.1 | 0.8s | **76** | | Gemma 2 27B | 71.3 | 44.6 | 0.6s | **63** | I’ve been running these tests through a structured multi-turn analysis framework I built to evaluate complex workflows. That’s how eTPS was stress-tested — not just single-turn benchmarks, but real back-and-forth sessions. Advanced mode (toggle) will add latency percentiles, cost-per-quality, and consistency scoring later. For v1 the goal is to keep it dead simple and immediately useful, even if you’re not deep into AI. The whole point is to cut through the noise and show which models actually deliver useful work, not just raw speed. What do you think should be added (or removed) for the first version? Any metrics you’d want to see front-and-center? **TL;DR:** Simple leaderboard with Raw TPS, eTPS, Time to First Token, and a clear Effectiveness Index. Advanced stuff stays hidden until you want it. Feedback welcome. submitted by /u/axendo [link] [comments]

eTPS — Effective Tokens Per Second: A Better Way to Measure Local LLM Performance

We're obsessed with raw tokens per second. Every hardware post leads with it. Every quantization comparison is ranked by it. It's the one number everyone agrees to report. It's also measuring the wrong thing. Raw TPS tells you how fast tokens hit the screen. It tells you almost nothing about how quickly you get a correct, usable answer. On sustained, multi-turn workflows, that gap becomes massive. A faster model that hallucinates, requires multiple corrections, and forgets context you gave it earlier can easily be less useful than a slower model that gets it right the first time. eTPS (Effective Tokens Per Second) is a complementary metric that measures actual progress toward a useful answer, not just token throughput. The basic idea: weight the final accepted output by how clean the path to that answer was — first-pass correct scores highest — then divide by total time. Correction loops, hallucinations, and repeated explanations all reduce the score. A response that never reaches a correct answer scores zero regardless of speed. It doesn't replace raw TPS. It sits next to it. Results — same prompt, four runs, same hardware: gemma-4-e2b (4.6B): 53.2 raw TPS → eTPS 53.18 ✓ qwen3.5-0.8b: 173.1 raw TPS → eTPS 86.57 ✗ partial qwen3.5-9b (optimized): 1.8 raw TPS → eTPS 1.78 ✓ qwen3.5-9b (baseline): 0.5 raw TPS → eTPS 0.32 ✗ partial The 0.8B leads on raw speed by a wide margin and still lost. Raw TPS said it won. eTPS said it didn't. Hardware: RTX 5060 Laptop, 8GB VRAM. eTPS scores aren't portable across hardware — always report your full setup. Known limitations (v0.1): Scoring requires human judgment. The line between "needed clarification" and "was factually wrong" isn't always clean. Code generation with objective pass/fail criteria is a cleaner target and the focus of the next benchmark run. One task isn't representative of sustained multi-turn workflows — that's where the metric gets most interesting and where I'm headed next. Easy to game without full system prompt logging. The spec will require it. These are acknowledged constraints, not hidden flaws. Full specification coming soon covering methodology, task library, scoring protocol, and reproducibility standards. Before I lock the final weights I'd genuinely like input on two open questions: How should the penalty differ between a model that confidently states something false versus one that's just vague enough you had to ask a follow-up? And should hardware normalization live in the core formula or be reported separately? Thoughts welcome. submitted by /u/axendo [link] [comments]

Offload routine Claude Code work to Gemma 4 through the Google GenAI API

The idea of offload-mcp is simple: instead of running hardware-hungry local models for routine work, let Claude offload that work to FREE model APIs and SAVE tokens. I’m using Gemma via the Google GenAI API because I like it in my processing pipelines, but running it locally on my MacBook Air is slow and resource-limited. The API path is much more practical for small jobs. I didn't find any other tool on GitHub or elsewhere to handle that. offload-mcp takes care of commit messages, PR summaries, translations, docstrings, source diff/file summaries, and freeform prompts. Freeform is what I use most: send almost any routine prompt to a cheaper model instead of burning expensive Claude Code or Codex context on it. The source-based mode can read local diffs/files directly through the MCP server and reports estimated primary input tokens avoided. The default model chain uses Gemma, but model IDs are configurable. Curious if this fits anyone else’s Claude workflow! GitHub: https://github.com/peterhadorn/offload-mcp submitted by /u/dd1100 [link] [comments]