Frontier labfresh 11h

Google (DeepMind / Gemini)

Signal timeline519 total
Jun 9, 2026
Jun 4, 2026
6dModelgoogle/gemma-4-12B-it-qat-q4_0-unquantized-assistantLow-traction quantized variant of Gemma 4sourcenotability 3.0/101.4k16
1wModelgoogle/gemma-4-12B-it-qat-q4_0-unquantizedNotable model release from Google DeepMind with moderate traction.sourcenotability 6.0/107.8k40
Jun 2, 2026
1wModelgoogle/CircularNetRoutine model release, lacking tractionsourcenotability 4.0/105
May 29, 2026
1wModelgoogle/gemma-4-31B-it-qat-q4_0-unquantized-assistantLow traction quantized variant of Gemma-4sourcenotability 5.0/101.5k14
1wModelgoogle/gemma-4-26B-A4B-it-qat-q4_0-unquantized-assistantLow traction routine model variant.sourcenotability 2.0/104897
1wModelgoogle/gemma-4-E4B-it-qat-q4_0-unquantized-assistantLow downloads, minor variantsourcenotability 2.0/102908
1wModelgoogle/gemma-4-E2B-it-qat-q4_0-unquantized-assistantLow traction variant releasesourcenotability 2.0/102226
May 28, 2026
1wModelgoogle/magenta-realtime-2Notable model release from DeepMind with solid traction.sourcenotability 7.0/1020k175
May 23, 2026
2wModelgoogle/gemma-4-12BNotable Gemma release with strong traction.sourcenotability 8.0/10140k505
2wModelgoogle/gemma-4-12B-it-assistantNotable model release from Google DeepMind with moderate downloads.sourcenotability 7.0/1023k81
2wModelgoogle/gemma-4-12B-itMajor lab model, high downloads.sourcenotability 8.0/10676k906
Apr 30, 2026
Apr 30Modelgoogle/gemma-4-E4B-it-qat-q4_0-unquantizedQuantized model release, modest traction.sourcenotability 3.0/101.7k9
Apr 29, 2026
Apr 29Modelgoogle/gemma-4-E2B-it-qat-q4_0-unquantizedLow-download model release, not notable.sourcenotability 3.0/102.9k13
Apr 29Modelgoogle/gemma-4-26B-A4B-it-qat-q4_0-unquantizedQuantized variant of Gemma, low downloads.sourcenotability 4.0/102.3k21
Apr 28, 2026
Apr 28Modelgoogle/gemma-4-31B-it-qat-q4_0-unquantizedModel from DeepMind, moderate traction.sourcenotability 6.0/102.8k23
Apr 23, 2026
Apr 23Modelgoogle/gemma-4-26B-A4B-it-assistantMajor lab release, high downloads.sourcenotability 8.0/10153k162
Apr 23Modelgoogle/gemma-4-31B-it-assistantHigh-download open model from DeepMindsourcenotability 8.0/10381k299
Apr 23Modelgoogle/gemma-4-E4B-it-assistantMajor lab release with strong community tractionsourcenotability 8.0/1088k108
Apr 23Modelgoogle/gemma-4-E2B-it-assistantNotable Gemma variant release, moderate traction.sourcenotability 7.0/1029k62
Apr 9, 2026
Apr 9Modelgoogle/tipsv2-g14-dptModel release, low tractionsourcenotability 4.0/1027314
Apr 9Modelgoogle/tipsv2-so400m14-dptLow traction, niche modelsourcenotability 4.0/101168
Apr 9Modelgoogle/tipsv2-l14-dptNotable model release from DeepMind with moderate downloads.sourcenotability 6.0/101.6k7
Apr 9Modelgoogle/tipsv2-b14-dptResearch model release with moderate tractionsourcenotability 5.0/1047914
Apr 9Modelgoogle/tipsv2-g14New model from DeepMind, moderate traction.sourcenotability 7.0/104k16

Top signals

  1. #1WritingA new era of intelligence with Gemini 310.0
  2. #2WritingGemma 4: Byte for byte, the most capable open models10.0
  3. #3WritingIntroducing Gemma 4 12B: a unified, encoder-free multimodal model10.0
  4. #4WritingStart building with Gemini 310.0
  5. #5WritingWe’re expanding our Gemini 2.5 family of models10.0

Agent answer

Google (DeepMind / Gemini) has 519 loaded public signals: 45 hiring, 0 forks, 208 releases or model cards, 105 talking, and 161 repos. Latest signal: DiffusionGemma: 4x faster text generation. Data-business radar maps 35 signals to Data demand, Evals and quality, Infrastructure, Safety and policy, Product and customer. The standing analysis was generated with an unknown model and 0 evidence refs.

Google (DeepMind / Gemini)

has loaded 519 public signals

Google (DeepMind / Gemini)

has hiring signal count 45

Google (DeepMind / Gemini)

has fork signal count 0

Google (DeepMind / Gemini)

has release signal count 208

Analysis — agent synthesisfull report →generated June 8, 2026

Thesis

Google DeepMind is running a two-front strategy: shipping the proprietary Gemini 2.5 frontier line (Pro, Flash, Flash-Lite, Deep Think, Computer Use) as the consumer/enterprise product, while seeding an open-weights ecosystem around the Gemma family on Hugging Face. In parallel it keeps pushing "AI for science" — genomics (AlphaGenome), fusion plasma control, gravitational-wave instrumentation, and single-cell biology — as proof points that its models do real-world discovery, not just chat.

Shipping

On Hugging Face the open-weights footprint is dominated by the Gemma 4 generation. `google/gemma-4-26B-A4B-it` is the clear leader at 12,161,679 30-day downloads (1,099 likes), an order of magnitude ahead of everything else. Behind it: `google/gemma-4-31B` (565,446), `google/gemma-4-12B-it` (434,969), `google/gemma-4-31B-it-assistant` (408,902), and `google/gemma-4-26B-A4B-it-assistant` (159,886). The tail shows the breadth of the open program: vision encoders `google/tipsv2-b14` (18,165) and its l14/g14 variants, plus generative audio in `google/magenta-realtime-2` (13,338).

On GitHub the most-starred repos reflect a research-tooling legacy more than the current Gemini push: `deepmind-research` (14,998 stars), `alphafold` (14,647), `mujoco` (13,791), `sonnet` (9,920), `alphafold3` (8,147), and `gemma` (5,356). Recent release activity is concentrated in the physics-sim and agent-tooling stack: `mujoco 3.9.0` and `mujoco_warp v3.9.0`, `gemma v4.0.1`, `onetwo v0.5.0`, `open_spiel v1.6.15`, and a `science-skills` package now at v1.0.2.

Research themes

Four themes recur across first-party writing:

Hiring & scaling

The 15 open roles point to investment in materials science and applied/agentic AI. Multiple materials/intelligence roles appear — "Research Scientist, Material Intelligence" (London), "Research Engineer, Materials Science" (Mountain View) — reinforcing the AI-for-science theme. A cluster of "Antigravity" roles ("Technical Program Manager, Antigravity" and "...Antigravity (Modeling & Evals)," both Mountain View) plus "Technical Program Manager, Agents Innovation" (London) and two "Applied AI" roles ("Staff Research Engineer, Applied AI" Singapore; "Manager, Applied AI Engineering" London) signal a build-out around agents, modeling/evals, and productization. Internationalization shows up directly in "Research Scientist: Multilingual, Multicultural and Multimodal LLM" (Tokyo), and embodied AI in "Research Scientist, HRI Research to Enable Collaborative Humanoid Robots" (New York City). Geographically the footprint spans Mountain View, London, Singapore, Tokyo, and NYC.

Traction highlights

Hacker News interest skews toward agents and embodied/scientific systems rather than the core Gemini model drops. Top threads: AlphaEvolve: How our Gemini-powered coding agent is scaling impact across fields (327 points, 149 comments), Reimagining the mouse pointer for the AI era (252 points, 213 comments), SIMA 2: An Agent that Plays, Reasons, and Learns With You in Virtual 3D Worlds (238 points), and Gemini Robotics-ER 1.6 (219 points). The `mujoco` repo also surfaced on HN (116 points). On raw distribution, the standout is `google/gemma-4-26B-A4B-it` at over 12.1M 30-day downloads; on GitHub stars, `deepmind-research` (14,998) and `alphafold` (14,647) lead.

Data-business radar

cross-lab →

35 matches · 5 active lanes

Google (DeepMind / Gemini) has a writing signal matching evals and quality, infrastructure.