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A permissive open-source software license from the Apache Software Foundation granting free use, modification, and distribution with patent rights.
Fivetran and dbt Labs said the Fusion runtime is now dbt Core v2.0 under Apache 2.0 when their merger closed on June 1, 2026. Teams that log it as open source need to check which artifact they deploy.
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Qwen released eight open-weight Qwen3 models, two MoE and six dense, that each support thinking and non-thinking modes. On the model card, one chat-template argument, enable_thinking, picks the mode and defaults to True.
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Aleph Alpha published Kolibri, a 78B-parameter English and German mixture-of-experts model, under Apache 2.0 with about 3.46B parameters active per token. Teams that cannot send prompts to outside servers can host it themselves if they pay for the GPUs.
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Google DeepMind's EmbeddingGemma 2 maps text, images, video and audio into one vector space using 740M Apache-licensed parameters. It is built to run on phones and laptops, so the case for a hosted embedding API now depends on whether its quality holds up on a buyer's own data.
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Google DeepMind released EmbeddingGemma 2, a 740M-parameter open model that runs on a phone and embeds text, code, images, video and audio in one space. Teams running a separate embedder per modality can consolidate on it if Google's reported benchmark numbers hold on their own data.
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Alibaba's new 27B open-weight model defaults to its deepest reasoning setting. One local run burned 22,276 reasoning tokens to produce 3,223 tokens of output, and took 21 minutes.
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Reflection AI says its first model, Beam, performs at the level of Z.ai's GLM-5.2 while using three to four times less compute than similar open models. Until outside testers report, those are the company's figures, and they count most with buyers who cannot use Chinese models.
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12 publishersReality
Google released EmbeddingGemma 2, an Apache 2.0 model that maps text, code, images, video and audio into one embedding space with 740M parameters. Phone apps get offline cross-media search from one set of weights, within limits set by a shared 8K-token window and lossy vector truncation.
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17 publishersReality
Reflection AI says Beam, its 501-billion-parameter open-weight model, needs three to four times less compute than comparable open models. Buyers have only the company's figures until the weights ship later in October.
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13 publishersReality
Meta released open source firmware and a Linux SDK for its Muse agent and is giving subscribers 5,000 Home Link dongles. For now the release suits people who tinker; companies hoping to sell Muse hardware have no published terms to build on.
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11 publishersReality
Interfaze released interfaze-1-lite, an Apache 2.0 open-weight model for OCR and speech that returns confidence scores and bounding boxes with its answers. Teams can send doubtful rows to a reviewer once they have tested how the scores behave on their own documents.
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Aleph Alpha released Kolibri, an Apache 2.0 German-English model that activates 3.46B of its 78.1B parameters per token. Each token costs about as much compute as a small model, yet a team hosting it in Europe still has to fit every expert in memory.
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6 publishersReality
Deployment code, checkpoints and a report for frame-by-frame editing, with a claimed 30 FPS end-to-end at 720x1248. That is a number a team can finally hold against its own budget.
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Omnara has replaced its remote-coding app with an Apache 2.0 control plane that keeps each agent's identity and history while worker machines come and go. Teams get one more self-hostable backend for agents run as durable services, though the only usage figures so far describe the retired app.
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Google Cloud AI Research has released RRSI, an Apache 2.0 tool whose self-rewriting agent harness lifted Terminal-Bench 2.1 scores from 74.2% to 80.2%. Any team can use it commercially, though on tasks the agent never trained against the reported gain falls to 4.7 points.
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EDG put the C++ front end it licensed to compiler makers for 30 years on GitHub under Apache 2.0, with the C++ Alliance as its non-profit home. Toolchain teams that bought it as a closed part can now patch it in public and pay jointly for new features.
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Intel has added NVIDIA's Apache-2.0 OpenShell policy layer to its enterprise agent toolkit, putting sandboxing and kernel-level agent controls on Xeon. The feature ships switched off, so what it is worth to Intel depends on how many agent teams turn it on.
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Supersonic Labs' Julia 1, a 144.3M-parameter model trained for about $104, picks labels from a caller's list on an ordinary CPU. On its own 72-label Banking77 pilot it scored 64 of 100, which shows where the 20-option cap keeps it from replacing an LLM classifier.
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IBM's 3B, 8B and 30B dense models all get a thinking switch and native tool calling, but only the two larger ones get agentic RL, and the tuning mixture leans hard on software engineering.
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3 publishersReality
Jared Palmer's Kev now ships as 0.8B, 4B and 9B variants that score typed answer options over frozen Qwen3.5 weights, and on his own development comparison hosted Jev still scores higher than the largest of them.
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