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@anjali shared a link, 10 months ago
Customer Marketing Manager, Last9

Improve Consistency Across Signals with OTel Semantic Conventions

Correlate logs, metrics, and traces faster by using consistent field names and schemas with OpenTelemetry semantic conventions.

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@anjali shared a link, 10 months ago
Customer Marketing Manager, Last9

How Replicas Work in Kubernetes

Understand how Kubernetes uses replicas to ensure your application stays available, handles traffic spikes, and recovers from pod failures automatically.

api metrics dashboard
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@arshadmas shared a post, 10 months ago
Product Marketer, manageengine

GCP monitoring: A comprehensive guide into maximizing cloud performance

Keeping mission-critical workloads healthy on Google Cloud Platform (GCP) isn’t optional—it’s your job. As organizations increasingly move to GCP for its elasticity and scalability, the complexity of managing cloud-native and hybrid environments grows. For ITOps and CloudOps professionals, the chall..

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@anjali shared a link, 10 months ago
Customer Marketing Manager, Last9

Instrument LangChain and LangGraph Apps with OpenTelemetry

Understand how to trace, monitor, and debug LangChain and LangGraph apps using OpenTelemetry, down to chains, tools, tokens, and state flows.

LangChain & LangGraph
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@faun shared a link, 10 months ago
FAUN.dev()

I’m Losing All Trust in the AI Industry

AI bigwigs promiseAGIin a quick 1-5 years, but the revolving door at labs like OpenAI screams wishful thinking. As AI hustles to serve up habit-forming products, the priority on user engagement echoes the well-troddensocial mediaplaybook. Who needs productivity, anyway? Cash fuels AI's joyride, with.. read more  

I’m Losing All Trust in the AI Industry
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@faun shared a link, 10 months ago
FAUN.dev()

EU businesses push for freedom from AI rules and competition

Mistral's"AI for Citizens" isn't just about tech; it's about shaking up public services for the better. Meanwhile, in the EU, a plot twist—50 European firms holler for halting the AI Act, all in the name of staying competitive. They argue speed matters more than red tape. But hey, watchdogs eye them.. read more  

EU businesses push for freedom from AI rules and competition
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@faun shared a link, 10 months ago
FAUN.dev()

From Noise to Structure: Building a Flow Matching Model from Scratch

Train a petite neural net to align velocity flows between distributions. DeployFlow Matching lossfor the job. Harness the precision of theAdamoptimizer to keep it sharp... read more  

From Noise to Structure: Building a Flow Matching Model from Scratch
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@faun shared a link, 10 months ago
FAUN.dev()

Gemma 3n Introduces Novel Techniques for Enhanced Mobile AI Inference

Gemma 3nshakes up mobile AI with a two-punch combo:Per-Layer Embeddingsthat axe RAM usage andMatFormerthat sends performance into overdrive with elastic inference and nesting.KV cache sharingcranks up the speed of streaming responses, though it taps out at multilingual audio processing for clips up .. read more  

Gemma 3n Introduces Novel Techniques for Enhanced Mobile AI Inference
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@faun shared a link, 10 months ago
FAUN.dev()

‘Shit in, shit out’: AI is coming for agriculture, but farmers aren’t convinced

Aussie farmers want "more automation, fewer bells and whistles"—technology should work like a tractor, not act like an app:straightforward, adaptable, and rock-solid... read more  

‘Shit in, shit out’: AI is coming for agriculture, but farmers aren’t convinced
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@faun shared a link, 10 months ago
FAUN.dev()

Automatically Evaluating AI Coding Assistants with Each Git Commit · TensorZero

TensorZerotransforms developer lives by nabbing feedback fromCursor'sLLM inferences. It dives into the details withtree edit distance (TED)to dissect code. Over in a different corner,Claude 3.7 SonnetschoolsGPT-4.1when it comes to personalized coding. Who knew? Not all AI flexes equally... read more  

Automatically Evaluating AI Coding Assistants with Each Git Commit · TensorZero
GPT-5.4 is OpenAI’s latest frontier AI model designed to perform complex professional and technical work more reliably. It combines advances in reasoning, coding, tool use, and long-context understanding into a single system capable of handling multi-step workflows across software environments. The model builds on earlier GPT-5 releases while integrating the strong coding capabilities previously introduced with GPT-5.3-Codex.

One of the defining features of GPT-5.4 is its ability to operate as part of agent-style workflows. The model can interact with tools, APIs, and external systems to complete tasks that extend beyond simple text generation. It also introduces native computer-use capabilities, allowing AI agents to operate applications using keyboard and mouse commands, screenshots, and browser automation frameworks such as Playwright.

GPT-5.4 supports context windows of up to one million tokens, enabling it to process and reason over very large documents, long conversations, or complex project contexts. This makes it suitable for tasks such as analyzing codebases, generating technical documentation, working with large spreadsheets, or coordinating long-running workflows. The model also introduces a feature called tool search, which allows it to dynamically retrieve tool definitions only when needed. This reduces token usage and makes it more efficient to work with large ecosystems of tools, including environments with dozens of APIs or MCP servers.

In addition to improved reasoning and automation capabilities, GPT-5.4 focuses on real-world productivity tasks. It performs better at generating and editing spreadsheets, presentations, and documents, and it is designed to maintain stronger context across longer reasoning processes. The model also improves factual accuracy and reduces hallucinations compared with previous versions.

GPT-5.4 is available across OpenAI’s ecosystem, including ChatGPT, the OpenAI API, and Codex. A higher-performance variant, GPT-5.4 Pro, is also available for users and developers who require maximum performance for complex tasks such as advanced research, large-scale automation, and demanding engineering workflows. Together, these capabilities position GPT-5.4 as a model aimed not just at conversation, but at executing real work across software systems.