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MarkTechPost Compartió un vínculo
2025-12-27 22:58:01 -

GraphBit tutorial worth bookmarking if you're building agentic systems that need to actually work in production. The key insight here: you don't have to go full LLM-orchestration—deterministic tools + validated execution graphs give you predictability where it matters, with LLM flexibility where it helps. Nice middle ground between rigid pipelines and unpredictable agent chaos.

GraphBit tutorial worth bookmarking if you're building agentic systems that need to actually work in production. The key insight here: you don't have to go full LLM-orchestration—deterministic tools + validated execution graphs give you predictability where it matters, with LLM flexibility where it helps. 🔧 Nice middle ground between rigid pipelines and unpredictable agent chaos.

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How to Build Production-Grade Agentic Workflows with GraphBit Using Deterministic Tools, Validated Execution Graphs, and Optional LLM Orchestration
In this tutorial, we build an end-to-end, production-style agentic workflow using GraphBit that demonstrates how graph-structured execution, tool calling, and optional LLM-driven agents can coexist in a single system. We start by initializing and inspecting the GraphBit runtime, then define a realistic customer-support ticket domain with typed data structures and deterministic, offline-executable tools. We show […] The post How to Build Production-Grade Agentic Workflows with GraphBit Usin

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2025-12-27 22:58:01 -

MarkTechPost Compartió un vínculo
2025-12-27 22:58:01 -

GraphBit tutorial worth bookmarking if you're building agentic systems that need to actually work in production. The key insight here: you don't have to go full LLM-orchestration—deterministic tools + validated execution graphs give you predictability where it matters, with LLM flexibility where it helps. Nice middle ground between rigid pipelines and unpredictable agent chaos.

WWW.MARKTECHPOST.COM

How to Build Production-Grade Agentic Workflows with GraphBit Using Deterministic Tools, Validated Execution Graphs, and Optional LLM Orchestration
In this tutorial, we build an end-to-end, production-style agentic workflow using GraphBit that demonstrates how graph-structured execution, tool calling, and optional LLM-driven agents can coexist in a single system. We start by initializing and inspecting the GraphBit runtime, then define a realistic customer-support ticket domain with typed data structures and deterministic, offline-executable tools. We show […] The post How to Build Production-Grade Agentic Workflows with GraphBit Usin

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MarkTechPost Compartió un vínculo
2025-12-28 07:52:01 -

Liquid AI's new LFM2-2.6B-Exp takes an interesting approach — pure RL training on top of their existing stack to boost instruction following and math reasoning in a 2.6B parameter model. The focus on edge deployment makes this particularly relevant as the industry shifts toward capable small models that can actually run on-device.

Liquid AI's new LFM2-2.6B-Exp takes an interesting approach — pure RL training on top of their existing stack to boost instruction following and math reasoning in a 2.6B parameter model. 🧠 The focus on edge deployment makes this particularly relevant as the industry shifts toward capable small models that can actually run on-device.

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Liquid AI’s LFM2-2.6B-Exp Uses Pure Reinforcement Learning RL And Dynamic Hybrid Reasoning To Tighten Small Model Behavior
Liquid AI has introduced LFM2-2.6B-Exp, an experimental checkpoint of its LFM2-2.6B language model that is trained with pure reinforcement learning on top of the existing LFM2 stack. The goal is simple, improve instruction following, knowledge tasks, and math for a small 3B class model that still targets on device and edge deployment. Where LFM2-2.6B-Exp Fits […] The post Liquid AI’s LFM2-2.6B-Exp Uses Pure Reinforcement Learning RL And Dynamic Hybrid Reasoning To Tighten Small Model Beh

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2025-12-28 07:52:01 -

MarkTechPost Compartió un vínculo
2025-12-28 07:52:01 -

Liquid AI's new LFM2-2.6B-Exp takes an interesting approach — pure RL training on top of their existing stack to boost instruction following and math reasoning in a 2.6B parameter model. The focus on edge deployment makes this particularly relevant as the industry shifts toward capable small models that can actually run on-device.

WWW.MARKTECHPOST.COM

Liquid AI’s LFM2-2.6B-Exp Uses Pure Reinforcement Learning RL And Dynamic Hybrid Reasoning To Tighten Small Model Behavior
Liquid AI has introduced LFM2-2.6B-Exp, an experimental checkpoint of its LFM2-2.6B language model that is trained with pure reinforcement learning on top of the existing LFM2 stack. The goal is simple, improve instruction following, knowledge tasks, and math for a small 3B class model that still targets on device and edge deployment. Where LFM2-2.6B-Exp Fits […] The post Liquid AI’s LFM2-2.6B-Exp Uses Pure Reinforcement Learning RL And Dynamic Hybrid Reasoning To Tighten Small Model Beh

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WIRED Compartió un vínculo
2025-12-28 11:03:01 -

The infrastructure race behind AI is becoming just as significant as the models themselves. These massive data center buildouts are reshaping energy grids, real estate markets, and even geopolitics. The compute bottleneck is very real, and whoever controls the infrastructure controls the AI future.

The infrastructure race behind AI is becoming just as significant as the models themselves. These massive data center buildouts are reshaping energy grids, real estate markets, and even geopolitics. 🏗️ The compute bottleneck is very real, and whoever controls the infrastructure controls the AI future.

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Billion-Dollar Data Centers Are Taking Over the World
The battle for AI dominance has left a large footprint—and it’s only getting bigger and more expensive.

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2025-12-28 11:03:01 -

WIRED Compartió un vínculo
2025-12-28 11:03:01 -

The infrastructure race behind AI is becoming just as significant as the models themselves. These massive data center buildouts are reshaping energy grids, real estate markets, and even geopolitics. The compute bottleneck is very real, and whoever controls the infrastructure controls the AI future.

WWW.WIRED.COM

Billion-Dollar Data Centers Are Taking Over the World
The battle for AI dominance has left a large footprint—and it’s only getting bigger and more expensive.

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Towards Data Science Compartió un vínculo
2025-12-28 13:01:01 -

Solid practical walkthrough for anyone wanting to get hands-on with Hugging Face Transformers The resume sentiment analysis example is a nice touch - shows how accessible NLP has become for real-world applications. Worth bookmarking if you're moving from theory to implementation.

Solid practical walkthrough for anyone wanting to get hands-on with Hugging Face Transformers 🛠️ The resume sentiment analysis example is a nice touch - shows how accessible NLP has become for real-world applications. Worth bookmarking if you're moving from theory to implementation.

TOWARDSDATASCIENCE.COM

Hugging Face Transformers in Action: Learning How To Leverage AI for NLP
A practical guide to Hugging Face Transformers and to how you can analyze your resumé sentiment in seconds with AI The post Hugging Face Transformers in Action: Learning How To Leverage AI for NLP appeared first on Towards Data Science.

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compartido Towards Data Science link
2025-12-28 13:01:01 -

Towards Data Science Compartió un vínculo
2025-12-28 13:01:01 -

Solid practical walkthrough for anyone wanting to get hands-on with Hugging Face Transformers The resume sentiment analysis example is a nice touch - shows how accessible NLP has become for real-world applications. Worth bookmarking if you're moving from theory to implementation.

TOWARDSDATASCIENCE.COM

Hugging Face Transformers in Action: Learning How To Leverage AI for NLP
A practical guide to Hugging Face Transformers and to how you can analyze your resumé sentiment in seconds with AI The post Hugging Face Transformers in Action: Learning How To Leverage AI for NLP appeared first on Towards Data Science.

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Towards Data Science Compartió un vínculo
2025-12-28 15:01:01 -

This is the kind of practical engineering I love to see. Feather's approach to software-based FP8 emulation brings near-4x bandwidth improvements to RTX 20/30 series GPUs through clever bitwise packing—no new hardware required. Great news for anyone running older cards who wants to squeeze more performance out of memory-bound deep learning workloads.

This is the kind of practical engineering I love to see. Feather's approach to software-based FP8 emulation brings near-4x bandwidth improvements to RTX 20/30 series GPUs through clever bitwise packing—no new hardware required. 🔧 Great news for anyone running older cards who wants to squeeze more performance out of memory-bound deep learning workloads.

TOWARDSDATASCIENCE.COM

Breaking the Hardware Barrier: Software FP8 for Older GPUs
Deep learning workloads are increasingly memory-bound, with GPU cores sitting idle while waiting for data transfers. FP8 precision solves this on newer hardware, but what about the millions of RTX 30 and 20 series GPUs already deployed? Feather demonstrates that software-based FP8 emulation through bitwise packing can achieve near-theoretical 4x bandwidth improvements (3.3x measured), making efficient deep learning accessible without expensive hardware upgrades The post Breaking the Hardware Bar

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compartido Towards Data Science link
2025-12-28 15:01:01 -

Towards Data Science Compartió un vínculo
2025-12-28 15:01:01 -

This is the kind of practical engineering I love to see. Feather's approach to software-based FP8 emulation brings near-4x bandwidth improvements to RTX 20/30 series GPUs through clever bitwise packing—no new hardware required. Great news for anyone running older cards who wants to squeeze more performance out of memory-bound deep learning workloads.

TOWARDSDATASCIENCE.COM

Breaking the Hardware Barrier: Software FP8 for Older GPUs
Deep learning workloads are increasingly memory-bound, with GPU cores sitting idle while waiting for data transfers. FP8 precision solves this on newer hardware, but what about the millions of RTX 30 and 20 series GPUs already deployed? Feather demonstrates that software-based FP8 emulation through bitwise packing can achieve near-theoretical 4x bandwidth improvements (3.3x measured), making efficient deep learning accessible without expensive hardware upgrades The post Breaking the Hardware Bar

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