#mitlicense — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #mitlicense, aggregated by home.social.
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RT @StepFun_ai: Wir stellen Step Code v0.1.0 vor.
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#CLI #Coding #DevelopmentTools #MITLicense #OpenSource #StepCode #arint_info
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RT @StepFun_ai: Wir stellen Step Code v0.1.0 vor.
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#CLI #Coding #DevelopmentTools #MITLicense #OpenSource #StepCode #arint_info
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RT @StepFun_ai: Wir stellen Step Code v0.1.0 vor.
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#CLI #Coding #DevelopmentTools #MITLicense #OpenSource #StepCode #arint_info
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Is this diagram / table correct and logical ? #agplv3 #epl20 #mitlicense #reticulum
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Is this diagram / table correct and logical ? #agplv3 #epl20 #mitlicense #reticulum
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Is this diagram / table correct and logical ? #agplv3 #epl20 #mitlicense #reticulum
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Is this diagram / table correct and logical ? #agplv3 #epl20 #mitlicense #reticulum
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Is this diagram / table correct and logical ? #agplv3 #epl20 #mitlicense #reticulum
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DuckDB stays open source as AWS agrees to buy its Amsterdam creator: Amsterdam engineers join AWS at closing, while a non-profit keeps the MIT license. Amazon disclosed no price, no date, no headcount. What it means for ad data. https://ppc.land/duckdb-stays-open-source-as-aws-agrees-to-buy-its-amsterdam-creator/ #DuckDB #AWS #OpenSource #DataAnalytics #MITLicense
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DuckDB stays open source as AWS agrees to buy its Amsterdam creator: Amsterdam engineers join AWS at closing, while a non-profit keeps the MIT license. Amazon disclosed no price, no date, no headcount. What it means for ad data. https://ppc.land/duckdb-stays-open-source-as-aws-agrees-to-buy-its-amsterdam-creator/ #DuckDB #AWS #OpenSource #DataAnalytics #MITLicense
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DuckDB stays open source as AWS agrees to buy its Amsterdam creator: Amsterdam engineers join AWS at closing, while a non-profit keeps the MIT license. Amazon disclosed no price, no date, no headcount. What it means for ad data. https://ppc.land/duckdb-stays-open-source-as-aws-agrees-to-buy-its-amsterdam-creator/ #DuckDB #AWS #OpenSource #DataAnalytics #MITLicense
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DuckDB stays open source as AWS agrees to buy its Amsterdam creator: Amsterdam engineers join AWS at closing, while a non-profit keeps the MIT license. Amazon disclosed no price, no date, no headcount. What it means for ad data. https://ppc.land/duckdb-stays-open-source-as-aws-agrees-to-buy-its-amsterdam-creator/ #DuckDB #AWS #OpenSource #DataAnalytics #MITLicense
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DuckDB stays open source as AWS agrees to buy its Amsterdam creator: Amsterdam engineers join AWS at closing, while a non-profit keeps the MIT license. Amazon disclosed no price, no date, no headcount. What it means for ad data. https://ppc.land/duckdb-stays-open-source-as-aws-agrees-to-buy-its-amsterdam-creator/ #DuckDB #AWS #OpenSource #DataAnalytics #MITLicense
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RT @ornith_: Aloha! 🌺 Wir stellen Ornith-1.5 vor, eine Familie von Open-Source-LLMs mit 9B Dense, 35B MoE und 397B MoE, trainiert mit selbstverbessernden Strategien. Es erreicht state-of-the-art Performance unter Open-Source-Modellen vergleichbarer Größe und liefert Leistung vergleichbar mit Claude Opus 4.8 bei Reasoning, agentic und Coding-Aufgaben: ✅Terminal-Bench 2.1 (86.1) ✅SWE-Bench (86 auf verified, 65.1 auf pro, 79.6 auf Multilingual) ✅DeepSWE (56) ✅HLE (44.6) ✅ClawEval (81.4) ✅Tool Decathlon (71.2) Ornith-1.5 macht einen großen Schritt hin zum Training von Foundation Models durch end-to-end Selbstverbesserung und erweitert die in Ornith-1.0 eingeführten Self-Scaffolding-Strategien zu einem vollständigeren Selbstverbesserungs-Loop: Das Modell schlägt neue Aufgaben vor, generiert aufgaben-spezifische Scaffolds und erzeugt Solution Rollouts für Reinforcement Learning, wodurch es kontinuierlich neue Lernerfahrungen schafft, aus denen es sich verbessern kann. Alle Modelle sowie ihre quantisierten Versionen (FP8, GGUF, MLX und NVFP4) wurden unter der MIT-Lizenz veröffentlicht, was uneingeschränkte kommerzielle und Forschungsnutzung ermöglicht. 📘Tech Blog: ornith.ai/ornith15.html 🤗Huggingface: huggingface.co/collections/o…
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#AIResearch #MachineLearning #MITLicense #OpenSourceLLM #Ornith15 #SelfImprovement #arint_info
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RT @ornith_: Aloha! 🌺 Wir stellen Ornith-1.5 vor, eine Familie von Open-Source-LLMs mit 9B Dense, 35B MoE und 397B MoE, trainiert mit selbstverbessernden Strategien. Es erreicht state-of-the-art Performance unter Open-Source-Modellen vergleichbarer Größe und liefert Leistung vergleichbar mit Claude Opus 4.8 bei Reasoning, agentic und Coding-Aufgaben: ✅Terminal-Bench 2.1 (86.1) ✅SWE-Bench (86 auf verified, 65.1 auf pro, 79.6 auf Multilingual) ✅DeepSWE (56) ✅HLE (44.6) ✅ClawEval (81.4) ✅Tool Decathlon (71.2) Ornith-1.5 macht einen großen Schritt hin zum Training von Foundation Models durch end-to-end Selbstverbesserung und erweitert die in Ornith-1.0 eingeführten Self-Scaffolding-Strategien zu einem vollständigeren Selbstverbesserungs-Loop: Das Modell schlägt neue Aufgaben vor, generiert aufgaben-spezifische Scaffolds und erzeugt Solution Rollouts für Reinforcement Learning, wodurch es kontinuierlich neue Lernerfahrungen schafft, aus denen es sich verbessern kann. Alle Modelle sowie ihre quantisierten Versionen (FP8, GGUF, MLX und NVFP4) wurden unter der MIT-Lizenz veröffentlicht, was uneingeschränkte kommerzielle und Forschungsnutzung ermöglicht. 📘Tech Blog: ornith.ai/ornith15.html 🤗Huggingface: huggingface.co/collections/o…
mehr auf Arint.info
#AIResearch #MachineLearning #MITLicense #OpenSourceLLM #Ornith15 #SelfImprovement #arint_info
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RT @ornith_: Aloha! 🌺 Wir stellen Ornith-1.5 vor, eine Familie von Open-Source-LLMs mit 9B Dense, 35B MoE und 397B MoE, trainiert mit selbstverbessernden Strategien. Es erreicht state-of-the-art Performance unter Open-Source-Modellen vergleichbarer Größe und liefert Leistung vergleichbar mit Claude Opus 4.8 bei Reasoning, agentic und Coding-Aufgaben: ✅Terminal-Bench 2.1 (86.1) ✅SWE-Bench (86 auf verified, 65.1 auf pro, 79.6 auf Multilingual) ✅DeepSWE (56) ✅HLE (44.6) ✅ClawEval (81.4) ✅Tool Decathlon (71.2) Ornith-1.5 macht einen großen Schritt hin zum Training von Foundation Models durch end-to-end Selbstverbesserung und erweitert die in Ornith-1.0 eingeführten Self-Scaffolding-Strategien zu einem vollständigeren Selbstverbesserungs-Loop: Das Modell schlägt neue Aufgaben vor, generiert aufgaben-spezifische Scaffolds und erzeugt Solution Rollouts für Reinforcement Learning, wodurch es kontinuierlich neue Lernerfahrungen schafft, aus denen es sich verbessern kann. Alle Modelle sowie ihre quantisierten Versionen (FP8, GGUF, MLX und NVFP4) wurden unter der MIT-Lizenz veröffentlicht, was uneingeschränkte kommerzielle und Forschungsnutzung ermöglicht. 📘Tech Blog: ornith.ai/ornith15.html 🤗Huggingface: huggingface.co/collections/o…
mehr auf Arint.info
#AIResearch #MachineLearning #MITLicense #OpenSourceLLM #Ornith15 #SelfImprovement #arint_info
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#PlugData folk? I am going through an iterative process of running at a wall and not managing to get passed it. I dust myself off and try again every month or three, but nothing has come of this after more than 8 months frustration.
I *really* want to try and work out how to make a State Variable Filter from #Vanilla components so that I can make an abstraction from it and so #HeavyCompiler can work with it.
It looks like no one has made one before that has shared it into any of the places which can be searched.
Slop answers are not helpful and confuse things. I am aware enough to see the immediate flaws when these are presented, but this is only helping in identifying what does not work.
Is there a #DSP person that uses either #PureData or PlugData out there who might be willing to offer some guidance?
This is for educational purposes and any result would want to be firmly grounded in CC and preferably an open #MITlicense.
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#PlugData folk? I am going through an iterative process of running at a wall and not managing to get passed it. I dust myself off and try again every month or three, but nothing has come of this after more than 8 months frustration.
I *really* want to try and work out how to make a State Variable Filter from #Vanilla components so that I can make an abstraction from it and so #HeavyCompiler can work with it.
It looks like no one has made one before that has shared it into any of the places which can be searched.
Slop answers are not helpful and confuse things. I am aware enough to see the immediate flaws when these are presented, but this is only helping in identifying what does not work.
Is there a #DSP person that uses either #PureData or PlugData out there who might be willing to offer some guidance?
This is for educational purposes and any result would want to be firmly grounded in CC and preferably an open #MITlicense.
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#PlugData folk? I am going through an iterative process of running at a wall and not managing to get passed it. I dust myself off and try again every month or three, but nothing has come of this after more than 8 months frustration.
I *really* want to try and work out how to make a State Variable Filter from #Vanilla components so that I can make an abstraction from it and so #HeavyCompiler can work with it.
It looks like no one has made one before that has shared it into any of the places which can be searched.
Slop answers are not helpful and confuse things. I am aware enough to see the immediate flaws when these are presented, but this is only helping in identifying what does not work.
Is there a #DSP person that uses either #PureData or PlugData out there who might be willing to offer some guidance?
This is for educational purposes and any result would want to be firmly grounded in CC and preferably an open #MITlicense.
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#PlugData folk? I am going through an iterative process of running at a wall and not managing to get passed it. I dust myself off and try again every month or three, but nothing has come of this after more than 8 months frustration.
I *really* want to try and work out how to make a State Variable Filter from #Vanilla components so that I can make an abstraction from it and so #HeavyCompiler can work with it.
It looks like no one has made one before that has shared it into any of the places which can be searched.
Slop answers are not helpful and confuse things. I am aware enough to see the immediate flaws when these are presented, but this is only helping in identifying what does not work.
Is there a #DSP person that uses either #PureData or PlugData out there who might be willing to offer some guidance?
This is for educational purposes and any result would want to be firmly grounded in CC and preferably an open #MITlicense.
-
#PlugData folk? I am going through an iterative process of running at a wall and not managing to get passed it. I dust myself off and try again every month or three, but nothing has come of this after more than 8 months frustration.
I *really* want to try and work out how to make a State Variable Filter from #Vanilla components so that I can make an abstraction from it and so #HeavyCompiler can work with it.
It looks like no one has made one before that has shared it into any of the places which can be searched.
Slop answers are not helpful and confuse things. I am aware enough to see the immediate flaws when these are presented, but this is only helping in identifying what does not work.
Is there a #DSP person that uses either #PureData or PlugData out there who might be willing to offer some guidance?
This is for educational purposes and any result would want to be firmly grounded in CC and preferably an open #MITlicense.
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RT @songqiaosu: 🐦 Ornith-1.0 model family has crossed 3M downloads on 🤗 @huggingface in two weeks of release. This milestone belongs to the community! Please leave any feedback in the comments! Every issue and PR will make Ornith stronger 💪 We'll open source and keep pushing the local LLM experience forward🫡 Ornith (@ornith_) Aloha! 🌺 Meet Ornith-1.0, a family of open-source LLMs specialized for agentic coding. Ornith-1.0 spans the full parameter sizes including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. It achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks including: ✅Terminal-Bench 2.1(77.5) ✅SWE-Bench(82.4 on verified, 62.2 on pro, 78.9 on Multilingual) ✅NL2Repo(48.2) ✅SWE Atlas(41.2 on QnA, 42.6 RF, 39.1 TW) ✅ClawEval(77.1) Post-trained on top of gemma4 and qwen3.5, Ornith-1.0 employs a novel self-improving training strategy in which reinforcement learning is used to generate not only solution rollouts, but also the task-specific scaffolds that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model generate higher-quality solutions in agentic coding.😎 All models are released under the MIT license, enabling full commercial and research use. 📖Tech Blog: deep-reinforce.com/ornith_1_… 🤗Huggingface: huggingface.co/collections/d… — https://nitter.net/ornith_/status/2070148887067963854#m
mehr auf Arint.info
#huggingface #Huggingface #make #MIT #MITlicense #nitter #opensource #qwen35 #SWE #SWEBench #arint_info
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RT @songqiaosu: 🐦 Ornith-1.0 model family has crossed 3M downloads on 🤗 @huggingface in two weeks of release. This milestone belongs to the community! Please leave any feedback in the comments! Every issue and PR will make Ornith stronger 💪 We'll open source and keep pushing the local LLM experience forward🫡 Ornith (@ornith_) Aloha! 🌺 Meet Ornith-1.0, a family of open-source LLMs specialized for agentic coding. Ornith-1.0 spans the full parameter sizes including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. It achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks including: ✅Terminal-Bench 2.1(77.5) ✅SWE-Bench(82.4 on verified, 62.2 on pro, 78.9 on Multilingual) ✅NL2Repo(48.2) ✅SWE Atlas(41.2 on QnA, 42.6 RF, 39.1 TW) ✅ClawEval(77.1) Post-trained on top of gemma4 and qwen3.5, Ornith-1.0 employs a novel self-improving training strategy in which reinforcement learning is used to generate not only solution rollouts, but also the task-specific scaffolds that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model generate higher-quality solutions in agentic coding.😎 All models are released under the MIT license, enabling full commercial and research use. 📖Tech Blog: deep-reinforce.com/ornith_1_… 🤗Huggingface: huggingface.co/collections/d… — https://nitter.net/ornith_/status/2070148887067963854#m
mehr auf Arint.info
#huggingface #Huggingface #make #MIT #MITlicense #nitter #opensource #qwen35 #SWE #SWEBench #arint_info
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RT @songqiaosu: 🐦 Ornith-1.0 model family has crossed 3M downloads on 🤗 @huggingface in two weeks of release. This milestone belongs to the community! Please leave any feedback in the comments! Every issue and PR will make Ornith stronger 💪 We'll open source and keep pushing the local LLM experience forward🫡 Ornith (@ornith_) Aloha! 🌺 Meet Ornith-1.0, a family of open-source LLMs specialized for agentic coding. Ornith-1.0 spans the full parameter sizes including 9B Dense, 31B Dense, 35B MoE, and 397B MoE. It achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks including: ✅Terminal-Bench 2.1(77.5) ✅SWE-Bench(82.4 on verified, 62.2 on pro, 78.9 on Multilingual) ✅NL2Repo(48.2) ✅SWE Atlas(41.2 on QnA, 42.6 RF, 39.1 TW) ✅ClawEval(77.1) Post-trained on top of gemma4 and qwen3.5, Ornith-1.0 employs a novel self-improving training strategy in which reinforcement learning is used to generate not only solution rollouts, but also the task-specific scaffolds that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model generate higher-quality solutions in agentic coding.😎 All models are released under the MIT license, enabling full commercial and research use. 📖Tech Blog: deep-reinforce.com/ornith_1_… 🤗Huggingface: huggingface.co/collections/d… — https://nitter.net/ornith_/status/2070148887067963854#m
mehr auf Arint.info
#huggingface #Huggingface #make #MIT #MITlicense #nitter #opensource #qwen35 #SWE #SWEBench #arint_info
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RT @ornith_: Aloha! 🌺 Lernen Sie Ornith-1.0 kennen: eine Familie von Open-Source-LLMs, die speziell für agentices Coding entwickelt wurde. Ornith-1.0 deckt das gesamte Spektrum der Parametergrößen ab, darunter 9B Dense, 31B Dense, 35B MoE und 397B MoE. Es erreicht unter Open-Source-Modellen vergleichbarer Größe state-of-the-art-Performance auf Coding-Benchmarks wie: ✅Terminal-Bench 2.1(77,5) ✅SWE-Bench(82,4 verifiziert, 62,2 Pro, 78,9 Multilingual) ✅NL2Repo(48,2) ✅SWE Atlas(41,2 QnA, 42,6 RF, 39,1 TW) ✅ClawEval(77,1) Nach dem Post-Training auf Basis von Gemma4 und Qwen3.5 setzt Ornith-1.0 eine neuartige, selbstverbessernde Trainingsstrategie ein, bei der Reinforcement Learning nicht nur zur Generierung von Lösungsausrollungen (Solution Rollouts), sondern auch zur Erstellung der diese Ausrollungen antreibenden, aufgaben-spezifischen Gerüste (Scaffolds) genutzt wird. Durch die gemeinsame Optimierung des Gerüsts und der daraus resultierenden Lösung generiert das Modell höherwertige Lösungen im agenticen Coding.😎 Alle Modelle stehen unter der MIT-Lizenz und ermöglichen uneingeschränkte kommerzielle sowie Forschungsanwendungen. 📖Tech-Blog: http://deep-reinforce.com/ornith10.html 🤗Huggingface: http://huggingface.co/collections/deepreinforce-ai/ornith-10
mehr auf Arint.info
#AgenticCoding #LLM #MachineLearning #MITLicense #OpenSource #arint_info
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RT @ornith_: Aloha! 🌺 Lernen Sie Ornith-1.0 kennen: eine Familie von Open-Source-LLMs, die speziell für agentices Coding entwickelt wurde. Ornith-1.0 deckt das gesamte Spektrum der Parametergrößen ab, darunter 9B Dense, 31B Dense, 35B MoE und 397B MoE. Es erreicht unter Open-Source-Modellen vergleichbarer Größe state-of-the-art-Performance auf Coding-Benchmarks wie: ✅Terminal-Bench 2.1(77,5) ✅SWE-Bench(82,4 verifiziert, 62,2 Pro, 78,9 Multilingual) ✅NL2Repo(48,2) ✅SWE Atlas(41,2 QnA, 42,6 RF, 39,1 TW) ✅ClawEval(77,1) Nach dem Post-Training auf Basis von Gemma4 und Qwen3.5 setzt Ornith-1.0 eine neuartige, selbstverbessernde Trainingsstrategie ein, bei der Reinforcement Learning nicht nur zur Generierung von Lösungsausrollungen (Solution Rollouts), sondern auch zur Erstellung der diese Ausrollungen antreibenden, aufgaben-spezifischen Gerüste (Scaffolds) genutzt wird. Durch die gemeinsame Optimierung des Gerüsts und der daraus resultierenden Lösung generiert das Modell höherwertige Lösungen im agenticen Coding.😎 Alle Modelle stehen unter der MIT-Lizenz und ermöglichen uneingeschränkte kommerzielle sowie Forschungsanwendungen. 📖Tech-Blog: http://deep-reinforce.com/ornith10.html 🤗Huggingface: http://huggingface.co/collections/deepreinforce-ai/ornith-10
mehr auf Arint.info
#AgenticCoding #LLM #MachineLearning #MITLicense #OpenSource #arint_info
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RT @ornith_: Aloha! 🌺 Lernen Sie Ornith-1.0 kennen: eine Familie von Open-Source-LLMs, die speziell für agentices Coding entwickelt wurde. Ornith-1.0 deckt das gesamte Spektrum der Parametergrößen ab, darunter 9B Dense, 31B Dense, 35B MoE und 397B MoE. Es erreicht unter Open-Source-Modellen vergleichbarer Größe state-of-the-art-Performance auf Coding-Benchmarks wie: ✅Terminal-Bench 2.1(77,5) ✅SWE-Bench(82,4 verifiziert, 62,2 Pro, 78,9 Multilingual) ✅NL2Repo(48,2) ✅SWE Atlas(41,2 QnA, 42,6 RF, 39,1 TW) ✅ClawEval(77,1) Nach dem Post-Training auf Basis von Gemma4 und Qwen3.5 setzt Ornith-1.0 eine neuartige, selbstverbessernde Trainingsstrategie ein, bei der Reinforcement Learning nicht nur zur Generierung von Lösungsausrollungen (Solution Rollouts), sondern auch zur Erstellung der diese Ausrollungen antreibenden, aufgaben-spezifischen Gerüste (Scaffolds) genutzt wird. Durch die gemeinsame Optimierung des Gerüsts und der daraus resultierenden Lösung generiert das Modell höherwertige Lösungen im agenticen Coding.😎 Alle Modelle stehen unter der MIT-Lizenz und ermöglichen uneingeschränkte kommerzielle sowie Forschungsanwendungen. 📖Tech-Blog: http://deep-reinforce.com/ornith10.html 🤗Huggingface: http://huggingface.co/collections/deepreinforce-ai/ornith-10
mehr auf Arint.info
#AgenticCoding #LLM #MachineLearning #MITLicense #OpenSource #arint_info
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RT @ornith_: Aloha! 🌺 Lernen Sie Ornith-1.0 kennen: eine Familie von Open-Source-LLMs, die speziell für agentices Coding entwickelt wurde. Ornith-1.0 deckt das gesamte Spektrum der Parametergrößen ab, darunter 9B Dense, 31B Dense, 35B MoE und 397B MoE. Es erreicht unter Open-Source-Modellen vergleichbarer Größe state-of-the-art-Performance auf Coding-Benchmarks wie: ✅Terminal-Bench 2.1(77,5) ✅SWE-Bench(82,4 verifiziert, 62,2 Pro, 78,9 Multilingual) ✅NL2Repo(48,2) ✅SWE Atlas(41,2 QnA, 42,6 RF, 39,1 TW) ✅ClawEval(77,1) Nach dem Post-Training auf Basis von Gemma4 und Qwen3.5 setzt Ornith-1.0 eine neuartige, selbstverbessernde Trainingsstrategie ein, bei der Reinforcement Learning nicht nur zur Generierung von Lösungsausrollungen (Solution Rollouts), sondern auch zur Erstellung der diese Ausrollungen antreibenden, aufgaben-spezifischen Gerüste (Scaffolds) genutzt wird. Durch die gemeinsame Optimierung des Gerüsts und der daraus resultierenden Lösung generiert das Modell höherwertige Lösungen im agenticen Coding.😎 Alle Modelle stehen unter der MIT-Lizenz und ermöglichen uneingeschränkte kommerzielle sowie Forschungsanwendungen. 📖Tech-Blog: http://deep-reinforce.com/ornith10.html 🤗Huggingface: http://huggingface.co/collections/deepreinforce-ai/ornith-10
mehr auf Arint.info
#AgenticCoding #LLM #MachineLearning #MITLicense #OpenSource #arint_info
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A nice surprise: the CUNY AI Lab has launched an AI Use Disclosure tool based on the GAIDeT (Generative AI Delegation Taxonomy) framework.
👉 https://tools.ailab.gc.cuny.edu/ai-disclosure/
Researchers can now generate publication-ready, structured disclosures of how generative #AI was used across different stages of the research workflow: from idea generation and literature review to data management and writing.
#OpenScience #ResearchIntegrity #GenAI #AcademicPublishing #GAIDeT #MITLicense
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A nice surprise: the CUNY AI Lab has launched an AI Use Disclosure tool based on the GAIDeT (Generative AI Delegation Taxonomy) framework.
👉 https://tools.ailab.gc.cuny.edu/ai-disclosure/
Researchers can now generate publication-ready, structured disclosures of how generative #AI was used across different stages of the research workflow: from idea generation and literature review to data management and writing.
#OpenScience #ResearchIntegrity #GenAI #AcademicPublishing #GAIDeT #MITLicense
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A nice surprise: the CUNY AI Lab has launched an AI Use Disclosure tool based on the GAIDeT (Generative AI Delegation Taxonomy) framework.
👉 https://tools.ailab.gc.cuny.edu/ai-disclosure/
Researchers can now generate publication-ready, structured disclosures of how generative #AI was used across different stages of the research workflow: from idea generation and literature review to data management and writing.
#OpenScience #ResearchIntegrity #GenAI #AcademicPublishing #GAIDeT #MITLicense
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A nice surprise: the CUNY AI Lab has launched an AI Use Disclosure tool based on the GAIDeT (Generative AI Delegation Taxonomy) framework.
👉 https://tools.ailab.gc.cuny.edu/ai-disclosure/
Researchers can now generate publication-ready, structured disclosures of how generative #AI was used across different stages of the research workflow: from idea generation and literature review to data management and writing.
#OpenScience #ResearchIntegrity #GenAI #AcademicPublishing #GAIDeT #MITLicense
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A nice surprise: the CUNY AI Lab has launched an AI Use Disclosure tool based on the GAIDeT (Generative AI Delegation Taxonomy) framework.
👉 https://tools.ailab.gc.cuny.edu/ai-disclosure/
Researchers can now generate publication-ready, structured disclosures of how generative #AI was used across different stages of the research workflow: from idea generation and literature review to data management and writing.
#OpenScience #ResearchIntegrity #GenAI #AcademicPublishing #GAIDeT #MITLicense
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RT @ZixuanLi_: GLM-5.2 wurde noch nicht offiziell vorgestellt (wie üblich werden wir eine formale Launch-Veranstaltung durchführen). Z.ai (@Zaiorg) Intelligence soll offen, zugänglich und bereit zum Bauen sein, um jeden Entwickler, überall, zu stärken. GLM-5.2 ist jetzt für alle GLM Coding Plan-Nutzer verfügbar, einschließlich Lite-, Pro-, Max- und Team-Plänen. docs.z.ai/devpack/latest-mod… Als unser neues Flaggschiff-Modell bietet GLM-5.2 leistungsstarke Coding-Fähigkeiten, Unterstützung für 1M-Kontexte und bewährte Stärken bei langfristigen Aufgaben. API- und Chatbot-Dienste werden nächste Woche gestartet. Das Modell wird ebenfalls nächste Woche unter der MIT-Lizenz offiziell als Open Source veröffentlicht. Die Zukunft der KI ist offen und gehört den Menschen. — https://nitter.net/Zaiorg/status/2065704919299235870#m
mehr auf Arint.info
#DeveloperTools #GLM52 #KIOpenSource #MITLicense #OpenAI #ZaiOrg #arint_info
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RT @ZixuanLi_: GLM-5.2 wurde noch nicht offiziell vorgestellt (wie üblich werden wir eine formale Launch-Veranstaltung durchführen). Z.ai (@Zaiorg) Intelligence soll offen, zugänglich und bereit zum Bauen sein, um jeden Entwickler, überall, zu stärken. GLM-5.2 ist jetzt für alle GLM Coding Plan-Nutzer verfügbar, einschließlich Lite-, Pro-, Max- und Team-Plänen. docs.z.ai/devpack/latest-mod… Als unser neues Flaggschiff-Modell bietet GLM-5.2 leistungsstarke Coding-Fähigkeiten, Unterstützung für 1M-Kontexte und bewährte Stärken bei langfristigen Aufgaben. API- und Chatbot-Dienste werden nächste Woche gestartet. Das Modell wird ebenfalls nächste Woche unter der MIT-Lizenz offiziell als Open Source veröffentlicht. Die Zukunft der KI ist offen und gehört den Menschen. — https://nitter.net/Zaiorg/status/2065704919299235870#m
mehr auf Arint.info
#DeveloperTools #GLM52 #KIOpenSource #MITLicense #OpenAI #ZaiOrg #arint_info
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RT @ZixuanLi_: GLM-5.2 wurde noch nicht offiziell vorgestellt (wie üblich werden wir eine formale Launch-Veranstaltung durchführen). Z.ai (@Zaiorg) Intelligence soll offen, zugänglich und bereit zum Bauen sein, um jeden Entwickler, überall, zu stärken. GLM-5.2 ist jetzt für alle GLM Coding Plan-Nutzer verfügbar, einschließlich Lite-, Pro-, Max- und Team-Plänen. docs.z.ai/devpack/latest-mod… Als unser neues Flaggschiff-Modell bietet GLM-5.2 leistungsstarke Coding-Fähigkeiten, Unterstützung für 1M-Kontexte und bewährte Stärken bei langfristigen Aufgaben. API- und Chatbot-Dienste werden nächste Woche gestartet. Das Modell wird ebenfalls nächste Woche unter der MIT-Lizenz offiziell als Open Source veröffentlicht. Die Zukunft der KI ist offen und gehört den Menschen. — https://nitter.net/Zaiorg/status/2065704919299235870#m
mehr auf Arint.info
#DeveloperTools #GLM52 #KIOpenSource #MITLicense #OpenAI #ZaiOrg #arint_info
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RT @XiaomiMiMo: 🚀 MiMo Code V0.1 ist jetzt live und quelloffen!
mehr auf Arint.info
#AICoding #DeveloperTools #MiMoCode #MITLicense #OpenSource #Xiaomi #arint_info
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RT @XiaomiMiMo: 🚀 MiMo Code V0.1 ist jetzt live und quelloffen!
mehr auf Arint.info
#AICoding #DeveloperTools #MiMoCode #MITLicense #OpenSource #Xiaomi #arint_info
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RT @AdinaYakup: Macaron-V1-Preview-749B 👀 ein Mixture-of-LoRA-Personal-Agent-Modell von MindLab
mehr auf Arint.info
#GenerativeUI #MacaronV1 #MindLab #MITLicense #MixtureOfLora #PersonalAgent #arint_info
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RT @AdinaYakup: Macaron-V1-Preview-749B 👀 ein Mixture-of-LoRA-Personal-Agent-Modell von MindLab
mehr auf Arint.info
#GenerativeUI #MacaronV1 #MindLab #MITLicense #MixtureOfLora #PersonalAgent #arint_info
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Ah, yes, because who doesn't want to spend a whole 90 seconds setting up yet another "life-changing" tool that promises to solve all your problems with the grace and elegance of a wrecking ball? 🌪️🔧 MIT-licensed, because clearly, licensing is what makes software better while GitHub is screaming "LOOK AT ALL THESE TOOLS!" like a toddler with a new toy box. 🎉💻
https://github.com/tracewayapp/traceway #lifechangingtools #softwarehumor #MITlicense #GitHubtools #techsatire #HackerNews #ngated -
Ah, yes, because who doesn't want to spend a whole 90 seconds setting up yet another "life-changing" tool that promises to solve all your problems with the grace and elegance of a wrecking ball? 🌪️🔧 MIT-licensed, because clearly, licensing is what makes software better while GitHub is screaming "LOOK AT ALL THESE TOOLS!" like a toddler with a new toy box. 🎉💻
https://github.com/tracewayapp/traceway #lifechangingtools #softwarehumor #MITlicense #GitHubtools #techsatire #HackerNews #ngated -
Ah, yes, because who doesn't want to spend a whole 90 seconds setting up yet another "life-changing" tool that promises to solve all your problems with the grace and elegance of a wrecking ball? 🌪️🔧 MIT-licensed, because clearly, licensing is what makes software better while GitHub is screaming "LOOK AT ALL THESE TOOLS!" like a toddler with a new toy box. 🎉💻
https://github.com/tracewayapp/traceway #lifechangingtools #softwarehumor #MITlicense #GitHubtools #techsatire #HackerNews #ngated -
Ah, yes, because who doesn't want to spend a whole 90 seconds setting up yet another "life-changing" tool that promises to solve all your problems with the grace and elegance of a wrecking ball? 🌪️🔧 MIT-licensed, because clearly, licensing is what makes software better while GitHub is screaming "LOOK AT ALL THESE TOOLS!" like a toddler with a new toy box. 🎉💻
https://github.com/tracewayapp/traceway #lifechangingtools #softwarehumor #MITlicense #GitHubtools #techsatire #HackerNews #ngated -
Ah, yes, because who doesn't want to spend a whole 90 seconds setting up yet another "life-changing" tool that promises to solve all your problems with the grace and elegance of a wrecking ball? 🌪️🔧 MIT-licensed, because clearly, licensing is what makes software better while GitHub is screaming "LOOK AT ALL THESE TOOLS!" like a toddler with a new toy box. 🎉💻
https://github.com/tracewayapp/traceway #lifechangingtools #softwarehumor #MITlicense #GitHubtools #techsatire #HackerNews #ngated -
TinyVU by Jun Murakami 🎛️
Compact VU meter, Waves-style accuracy, multi-format, open-source (MIT)💻 macOS/Win/Linux (VST3/AU/AAX/LV2/CLAP)
🎁 FREE https://github.com/Jun-Murakami/TinyVU/releasesMore freeware 👉 https://linktr.ee/legalvst
#freeplugin #vumeter #opensource #mitlicense #analyzerplugin #junmurakami #compactplugin
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TinyVU by Jun Murakami 🎛️
Compact VU meter, Waves-style accuracy, multi-format, open-source (MIT)💻 macOS/Win/Linux (VST3/AU/AAX/LV2/CLAP)
🎁 FREE https://github.com/Jun-Murakami/TinyVU/releasesMore freeware 👉 https://linktr.ee/legalvst
#freeplugin #vumeter #opensource #mitlicense #analyzerplugin #junmurakami #compactplugin
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TinyVU by Jun Murakami 🎛️
Compact VU meter, Waves-style accuracy, multi-format, open-source (MIT)💻 macOS/Win/Linux (VST3/AU/AAX/LV2/CLAP)
🎁 FREE https://github.com/Jun-Murakami/TinyVU/releasesMore freeware 👉 https://linktr.ee/legalvst
#freeplugin #vumeter #opensource #mitlicense #analyzerplugin #junmurakami #compactplugin
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TinyVU by Jun Murakami 🎛️
Compact VU meter, Waves-style accuracy, multi-format, open-source (MIT)💻 macOS/Win/Linux (VST3/AU/AAX/LV2/CLAP)
🎁 FREE https://github.com/Jun-Murakami/TinyVU/releasesMore freeware 👉 https://linktr.ee/legalvst
#freeplugin #vumeter #opensource #mitlicense #analyzerplugin #junmurakami #compactplugin
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TinyVU by Jun Murakami 🎛️
Compact VU meter, Waves-style accuracy, multi-format, open-source (MIT)💻 macOS/Win/Linux (VST3/AU/AAX/LV2/CLAP)
🎁 FREE https://github.com/Jun-Murakami/TinyVU/releasesMore freeware 👉 https://linktr.ee/legalvst
#freeplugin #vumeter #opensource #mitlicense #analyzerplugin #junmurakami #compactplugin
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#Xiaomi released two #opensource #AImodels, #MiMoV25 and Pro, under the #MITLicense. These models are efficient for agentic “claw” tasks, such as those performed by #OpenClaw and #NanoClaw, and are priced competitively. The Pro model boasts a 63.8% success rate on the #ClawEval benchmark, outperforming #Anthropic and #OpenAI. https://venturebeat.com/ai/open-source-xiaomi-mimo-v2-5-and-v2-5-pro-are-among-the-most-efficient-and-affordable-at-agentic-claw-tasks?eicker.news #tech #media #news
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#Xiaomi released two #opensource #AImodels, #MiMoV25 and Pro, under the #MITLicense. These models are efficient for agentic “claw” tasks, such as those performed by #OpenClaw and #NanoClaw, and are priced competitively. The Pro model boasts a 63.8% success rate on the #ClawEval benchmark, outperforming #Anthropic and #OpenAI. https://venturebeat.com/ai/open-source-xiaomi-mimo-v2-5-and-v2-5-pro-are-among-the-most-efficient-and-affordable-at-agentic-claw-tasks?eicker.news #tech #media #news
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#Xiaomi released two #opensource #AImodels, #MiMoV25 and Pro, under the #MITLicense. These models are efficient for agentic “claw” tasks, such as those performed by #OpenClaw and #NanoClaw, and are priced competitively. The Pro model boasts a 63.8% success rate on the #ClawEval benchmark, outperforming #Anthropic and #OpenAI. https://venturebeat.com/ai/open-source-xiaomi-mimo-v2-5-and-v2-5-pro-are-among-the-most-efficient-and-affordable-at-agentic-claw-tasks?eicker.news #tech #media #news
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#Xiaomi released two #opensource #AImodels, #MiMoV25 and Pro, under the #MITLicense. These models are efficient for agentic “claw” tasks, such as those performed by #OpenClaw and #NanoClaw, and are priced competitively. The Pro model boasts a 63.8% success rate on the #ClawEval benchmark, outperforming #Anthropic and #OpenAI. https://venturebeat.com/ai/open-source-xiaomi-mimo-v2-5-and-v2-5-pro-are-among-the-most-efficient-and-affordable-at-agentic-claw-tasks?eicker.news #tech #media #news
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#Xiaomi released two #opensource #AImodels, #MiMoV25 and Pro, under the #MITLicense. These models are efficient for agentic “claw” tasks, such as those performed by #OpenClaw and #NanoClaw, and are priced competitively. The Pro model boasts a 63.8% success rate on the #ClawEval benchmark, outperforming #Anthropic and #OpenAI. https://venturebeat.com/ai/open-source-xiaomi-mimo-v2-5-and-v2-5-pro-are-among-the-most-efficient-and-affordable-at-agentic-claw-tasks?eicker.news #tech #media #news
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Cal.com Goes Closed Source: What It Means for Self Hosting, Trust, and Open Software in 2026
Cal.com is taking its production code private while launching Cal.diy. Here is what the change means for self hosting, trust, and user control. -
Cal.com Goes Closed Source: What It Means for Self Hosting, Trust, and Open Software in 2026
Cal.com is taking its production code private while launching Cal.diy. Here is what the change means for self hosting, trust, and user control. -
The QR codes I’ve been posting lately were created with my own app: QR Studio ULTRA.
I released it under the MIT License, which means anyone, anywhere can use it free.
#MITLicense #QRCode #FreeSoftware #Indiedev
Go to the link below scroll down and grab the release .APK
https://github.com/Cypher-Shadowbourne/qr-studio-ultra/releases/tag/v1.0.0 -
The QR codes I’ve been posting lately were created with my own app: QR Studio ULTRA.
I released it under the MIT License, which means anyone, anywhere can use it free.
#MITLicense #QRCode #FreeSoftware #Indiedev
Go to the link below scroll down and grab the release .APK
https://github.com/Cypher-Shadowbourne/qr-studio-ultra/releases/tag/v1.0.0 -
The QR codes I’ve been posting lately were created with my own app: QR Studio ULTRA.
I released it under the MIT License, which means anyone, anywhere can use it free.
#MITLicense #QRCode #FreeSoftware #Indiedev
Go to the link below scroll down and grab the release .APK
https://github.com/Cypher-Shadowbourne/qr-studio-ultra/releases/tag/v1.0.0