🚨 NEWS: Krea rilascia Krea 2 Raw e Krea 2 Turbo in open weights con licenza custom per uso enterprise

Ecco i punti chiave in breve:
💡 L'intelligenza artificiale generativa per immagini ha spesso un problema di fondo: le produzioni tendono a essere anonime, tutte simili, prive di personalità. Molte aziende che int...

🚀 LINK: https://meteoraweb.com/news/krea-rilascia-krea-2-raw-e-krea-2-turbo-in-open-weights-con-licenza-custom-per-uso-enterprise

#deepLearning #intelligenzaArtificiale #generazioneImmaginiAI #krea2 #openWeights

"Reliance on proprietary AI platforms can create long-term operational dependencies. As systems become less interoperable, organizations may be forced to standardize on a single stack across data pipelines, models, and decision logic, says the Linux Foundation’s Collier.

“As infrastructure consolidates, enterprises become more exposed when platforms change direction, raise prices, or fall behind technically,” he says. “If you can’t change platforms without re-architecting your AI systems, you’ve already given up too much control.”

“When you build on someone else’s platform, you have to live by their rules and those rules always change,” adds WordPress VIP’s Alvey. “We’ve all seen this before, businesses wasting time and money building to serve Google, Facebook, YouTube, and the App Store, instead of building to serve their customers.”

Platform lock-in can also create direct business risk. As Ainekko’s Shaposhnik says, “It usually shows up as higher costs, fragile systems, and growing risk when it’s time to change direction.”"

https://www.infoworld.com/article/4186382/why-open-infrastructure-will-define-the-ai-era.html

#OpenInfrastructure #AI #GenerativeAI #OpenSource #LockIn #Rents #Rentism #OpenWeights

"Others encourage strategies to reduce proprietary AI dependency. “Beware of vendor lock-in, and use abstraction layers where possible,” Manik Surtani, CTO and co-founder of the Agentic AI Foundation (AAIF), tells LeadDev.

Instead of Anthropic’s Claude Code or OpenAI’s Codex, he recommends Goose, an open-source AI agent from AAIF. “Goose gives you flexibility of model, to help you navigate pricing changes, pivots and deprecation, government bans,” he says, noting it can run multiple models concurrently.

Another benefit to using an open-source agent is you get a vendor-neutral harness. “Goose isn’t incentivized to be token-verbose, which you can’t say about some proprietary agent harnesses,” he adds.

Another hedge against commercial vendor shifts is using open-source and open-weight models. This especially makes sense for simpler operations where state-of-the-art frontier models are unnecessary.

“Companies need digital autonomy – and that starts with open-source models,” says Keus. “Open-source models give you the portability and control to build a truly model-agnostic foundation.”

For him, the benefits are the ability to self-host, switch, audit, and fork models without rebuilding your infrastructure.

“At GreenPT, we run everything on open source,” adds Keus. “Even the newest coding models like GLM5.2 and Kimi2.7-code, are really good. We see that teams are switching more often.”"

https://leaddev.com/ai/ai-models-can-disappear-overnight-is-your-engineering-team-built-to-survive-it

#AI #GenerativeAI #LLMs #OpenWeights #OpenSource #CodingModels #AICoding

Most advanced open-weights language model - made in China, of course!!

"We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a solid 1M-token context. GLM-5.2's new capabilities include:

- Solid 1M Context: A solid 1M-token context that stably sustains long-horizon work

- Advanced Coding with Flexible Effort: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency

- Improved Architecture: We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length.
We also improve GLM-5.2’s MTP layer for speculative decoding, increasing the acceptance length by up to 20%

- Pure Open: An MIT open-source license — no regional limits, technical access without borders

Supporting long-horizon tasks starts with making long context engineering-usable: the model must maintain quality across long, messy coding-agent trajectories, not just accept more tokens. A 1M context is easy to claim, but much harder to keep reliable under real engineering pressure. To this end, we substantially expanded 1M-context training for coding-agent scenarios, covering large-scale implementation, automated research, performance optimization, and complex debugging. The result is a long-context system that is not only wide in scope, but solid in execution: a practical substrate for sustained engineering work."

https://z.ai/blog/glm-5.2

#AI #GenerativeAI #LLMs #OpenWeights #OpenSource #China

GLM-5.2 is officially flexing on the open weights scene. It's not just better, it feels like a generational leap in reasoning capability. The competition just got *really* spicy. 🤯

#AI #LLMs #OpenWeights #TechNews

GLM-5.2 is officially flexing on the open weights scene. It's not just better, it feels like a generational leap in reasoning capability. The competition just got *really* spicy. 🤯

#AI #LLMs #OpenWeights #TechNews

GLM-5.2 is the new leading open weights model on Artificial Analysis

https://artificialanalysis.ai/articles/glm-5-2-is-the-new-leading-open-weights-model-on-the-artificial-analysis-intelligence-index

#HackerNews #GLM52 #ArtificialIntelligence #OpenWeights #MachineLearning #AIModels

RT @Zai_org: Wir stellen GLM-5.2 vor: Intelligenz an der Frontierspitze, offene Gewichte

mehr auf Arint.info

#API #GLM5 #KI #MaschinellesLernen #OpenWeights #TechBlog #arint_info

https://x.com/Zai_org/status/2066938937344495629#m

Last week Anthropic shipped its most capable models. Days later a government order pulled them, and every customer who built on them lost access overnight, with no say and no recourse.

That single event is the argument of my new post: A frontier-lab API does not belong inside your trusted computing base. The reason is not that the lab is malicious. A lab acting in complete good faith is still an unsafe foundation, because everything that matters about it can change while your code stays exactly as it was. The vendor resets the price at will. Refusals widen without warning, and the model itself can vanish on a government order you had no part in.

Open weights are the only architecture that keeps the thing you depend on auditable, forkable, and yours. Run them on your own hardware and you take every government, the chaotic one and the stable one alike, out of your execution loop.

The post also covers the token-cost crisis now forcing companies to ration AI spend, Anthropic's short-lived safeguard built to covertly degrade output, and why saving your own reasoning traces is what lets you leave a vendor you no longer trust.

Read the full article: https://www.provos.org/p/case-for-open-weight-models/

#AI #OpenWeights #LLMSecurity