The A.I Megathread (LLM , GPT , Development)

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1/19
@ChrisClickUp
The $3B feature Google couldn't stop:

OpenAI's record-breaking acquisition wasn't for an AI assistant:

They found a way to modify specific lines of code without full codebase access.

Here's why this threatens every company's software security:

2/19
@ChrisClickUp
Most code edits today require full codebase access.

You need to see the entire program to make targeted changes.

Think of it like needing to examine an entire car just to replace a faulty headlight.

But what if you could fix that headlight without seeing the rest of the car?



https://video.twimg.com/amplify_video/1920160422625488898/vid/avc1/1280x720/jtKJdydy-6dlE0yV.mp4

3/19
@ChrisClickUp
That's what OpenAI acquired with their $3B purchase of Windsurf (formerly Codeium).

Their tech lets AI modify specific code sections without seeing the entire codebase.

This changes everything about software security...

4/19
@ChrisClickUp
Imagine a locksmith who can replace just one tumbler in your lock without taking the whole thing apart.

Sounds convenient, right?

But what if that locksmith is a thief?

That's the double-edged sword of this technology:

5/19
@ChrisClickUp
The tech works by creating a "mental model" of how code functions.

It allows users to modify individual lines through natural language prompts.

No need to download or review the entire codebase.



https://video.twimg.com/amplify_video/1920160525029421056/vid/avc1/1280x720/dZeRjlmtmPgSFSWL.mp4

6/19
@ChrisClickUp
Traditional security models rely on "security through obscurity."

Companies keep their codebases private, assuming hackers need to see the whole system to exploit it.

That assumption just collapsed.

With Windsurf's technology, attackers could potentially launch attacks with minimal code exposure.



7/19
@ChrisClickUp
Why did OpenAI pay $3 billion – more than double Windsurf's previous $1.25 billion valuation?

Because it fundamentally changes software development:

• Faster code fixes without complex system understanding
• More accessible programming for non-experts
• Automated updates across massive codebases

But these benefits come with serious security risks:



https://video.twimg.com/amplify_video/1920160573872099328/vid/avc1/1280x720/l4tyQIOukpcnnaD8.mp4

8/19
@ChrisClickUp
Every software vulnerability becomes more dangerous.

Previously, hackers finding a security flaw still needed to understand the surrounding code to exploit it.

Now, AI could potentially generate working exploits from minimal information.

This creates powerful advantages for malicious actors.



https://video.twimg.com/amplify_video/1920160621590712321/vid/avc1/1280x720/cecxG1HQ3ytPBRXA.mp4

9/19
@ChrisClickUp
• Small code leaks become major vulnerabilities
• Legacy systems become easier to compromise
• Supply chain attacks grow more sophisticated
• Open-source contributions could hide malicious code

Google reportedly tried to acquire similar technologies but couldn't match OpenAI's move.

Why were they so desperate?



https://video.twimg.com/amplify_video/1920160664288702465/vid/avc1/1280x720/-QsvBItwtr2AV3sR.mp4

10/19
@ChrisClickUp
Because whoever controls this capability shapes the future of code security.

Before being acquired, Windsurf offered free usage for developers who supplied their API keys.

The company had also partnered with several Fortune 500 firms under strict NDAs.

The security implications are enormous:



https://video.twimg.com/amplify_video/1920160696157024256/vid/avc1/1280x720/6UxQaX2rkYVPodC9.mp4

11/19
@ChrisClickUp
According to industry analysts, Windsurf's technology represents one of the most significant shifts in software security in years.

Companies must now assume that small code snippets could compromise their entire systems.



https://video.twimg.com/amplify_video/1920160762116571136/vid/avc1/1280x720/Z7qPhEnM6XJtktFu.mp4

12/19
@ChrisClickUp
Traditional security practices like:
• Code obfuscation
• Limiting repository access
• Segmenting codebases

Are no longer sufficient protections.

What should companies do instead?



13/19
@ChrisClickUp
The most effective defense will be comprehensive runtime monitoring and behavioral analysis.

Since preventing code access becomes less effective, detecting unusual behavior becomes essential.

Companies should implement:
• Simulation environments for testing changes
• Automated static analysis
• Zero-trust architecture



https://video.twimg.com/amplify_video/1920160809621336066/vid/avc1/1280x720/AfUv6WIuh8HrRgTY.mp4

14/19
@ChrisClickUp
This acquisition signals the beginning of a new arms race between AI-powered development and AI-powered security.

Companies that adapt quickly will thrive.

Those that cling to outdated security models will find themselves increasingly vulnerable.

The era of "security through obscurity" is officially over.



15/19
@ChrisClickUp
As AI continues to revolutionize code creation and modification, we're seeing a fundamental shift in how companies must approach security.

The old playbook of protecting your codebase is becoming obsolete.

What matters now is understanding how your code behaves when it runs - and identifying anomalies before they become breaches.



16/19
@ChrisClickUp
This shift requires a new generation of security tools designed specifically for the AI era.

Tools that can monitor behavior patterns in real-time.

Tools that can detect subtle code modifications that traditional security measures would miss.

Tools built by people who understand both AI and security at a fundamental level.



https://video.twimg.com/amplify_video/1920160883822804992/vid/avc1/1280x720/ARxoaJojQjvjfutd.mp4

17/19
@ChrisClickUp
That's why I've been obsessively tracking these developments in AI security for years.

Each breakthrough - from automated coding to this new surgical code modification capability - creates both possibilities and vulnerabilities.

By understanding where this technology is headed, we can build better protections and smarter systems.



18/19
@ChrisClickUp
Want to stay ahead of these emerging AI security threats and opportunities?

Follow me for weekly insights on AI developments that impact your business security.

I share practical strategies to protect your systems in this rapidly evolving landscape.



19/19
@ChrisClickUp
Video credits:
Deirdre Bosa - CNBC:
Y Combinator:
AI LABS:
Low Level:
TED:




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1/6
@kevinhou22
goodbye runbooks, hello /workflows

[1/5] absolutely LOVING this new feature in windsurf. Some of my use cases so far:

- deploy my server to kubernetes
- generate PRs using my team's style
- get the recent error logs

The possibilities are endless. Here's how it works

2/6
@kevinhou22
[2/5] Windsurf rules already provide LLMs with guidance via persistent, reusable context at the prompt level.

Workflows extend this concept with:
- structured sequences of prompts on a per step level
- chaining interconnected tasks / actions
- general enough to handle ambiguity

3/6
@kevinhou22
[3/5] It's super simple to setup a new /workflow:

1. Click "Customize" --> "Workflows"
2. Press "+Workflow" to create a new one
3. Add a series of steps that Windsurf can follow
4. Set a title & description

The best part is, you can write it all in text!



https://video.twimg.com/amplify_video/1920197094142521344/vid/avc1/3652x2160/a14EBqmsj3lhtuei.mp4

4/6
@kevinhou22
[4/5] You can also ask Windsurf to generate Workflows for you! This works particularly well for workflows involving a series of steps in a particular CLI tool.

Check it out:



https://video.twimg.com/amplify_video/1920197704044720129/vid/avc1/3696x2160/AyMiwDXJ28XE1Xy0.mp4

5/6
@kevinhou22
[5/5] To execute a workflow, users simply invoke it in Cascade using the /[workflow-name] command.

It's that easy


Check it out on @windsurf_ai v1.8.2 today
Windsurf (formerly Codeium) - The most powerful AI Code Editor



6/6
@lyson_ober
Respect 🫡 Like it




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Google Releases 76-Page Whitepaper on AI Agents: A Deep Technical Dive into Agentic RAG, Evaluation Frameworks, and Real-World Architectures

Google Releases 76-Page Whitepaper on AI Agents: A Deep Technical Dive into Agentic RAG, Evaluation Frameworks, and Real-World Architectures

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Google Releases 76-Page Whitepaper on AI Agents: A Deep Technical Dive into Agentic RAG, Evaluation Frameworks, and Real-World Architectures​

By Sana Hassan

May 6, 2025

Google has published the second installment in its Agents Companion series—an in-depth 76-page whitepaper aimed at professionals developing advanced AI agent systems. Building on foundational concepts from the first release, this new edition focuses on operationalizing agents at scale, with specific emphasis on agent evaluation, multi-agent collaboration, and the evolution of Retrieval-Augmented Generation ( RAG ) into more adaptive, intelligent pipelines.

Agentic RAG: From Static Retrieval to Iterative Reasoning​

At the center of this release is the evolution of RAG architectures. Traditional RAG pipelines typically involve static queries to vector stores followed by synthesis via large language models. However, this linear approach often fails in multi-perspective or multi-hop information retrieval.

Agentic RAG reframes the process by introducing autonomous retrieval agents that reason iteratively and adjust their behavior based on intermediate results. These agents improve retrieval precision and adaptability through:

• Context-Aware Query Expansion : Agents reformulate search queries dynamically based on evolving task context.
• Multi-Step Decomposition : Complex queries are broken into logical subtasks, each addressed in sequence.
• Adaptive Source Selection : Instead of querying a fixed vector store, agents select optimal sources contextually.
• Fact Verification : Dedicated evaluator agents validate retrieved content for consistency and grounding before synthesis.

The net result is a more intelligent RAG pipeline, capable of responding to nuanced information needs in high-stakes domains such as healthcare, legal compliance, and financial intelligence.

Rigorous Evaluation of Agent Behavior​

Evaluating the performance of AI agents requires a distinct methodology from that used for static LLM outputs. Google’s framework separates agent evaluation into three primary dimensions:

1. Capability Assessment : Benchmarking the agent’s ability to follow instructions, plan, reason, and use tools. Tools like AgentBench, PlanBench, and BFCL are highlighted for this purpose.
2. Trajectory and Tool Use Analysis : Instead of focusing solely on outcomes, developers are encouraged to trace the agent’s action sequence (trajectory) and compare it to expected behavior using precision, recall, and match-based metrics.
3. Final Response Evaluation : Evaluation of the agent’s output through autoraters—LLMs acting as evaluators—and human-in-the-loop methods. This ensures that assessments include both objective metrics and human-judged qualities like helpfulness and tone.

This process enables observability across both the reasoning and execution layers of agents, which is critical for production deployments.

Scaling to Multi-Agent Architectures​

As real-world systems grow in complexity, Google’s whitepaper emphasizes a shift toward multi-agent architectures , where specialized agents collaborate, communicate, and self-correct.

Key benefits include:

• Modular Reasoning : Tasks are decomposed across planner, retriever, executor, and validator agents.
• Fault Tolerance : Redundant checks and peer hand-offs increase system reliability.
• Improved Scalability : Specialized agents can be independently scaled or replaced.

Evaluation strategies adapt accordingly. Developers must track not only final task success but also coordination quality, adherence to delegated plans, and agent utilization efficiency. Trajectory analysis remains the primary lens, extended across multiple agents for system-level evaluation.

Real-World Applications: From Enterprise Automation to Automotive AI​

The second half of the whitepaper focuses on real-world implementation patterns:

AgentSpace and NotebookLM Enterprise​

Google’s AgentSpace is introduced as an enterprise-grade orchestration and governance platform for agent systems. It supports agent creation, deployment, and monitoring, incorporating Google Cloud’s security and IAM primitives. NotebookLM Enterprise, a research assistant framework, enables contextual summarization, multimodal interaction, and audio-based information synthesis.

Automotive AI Case Study​

A highlight of the paper is a fully implemented multi-agent system within a connected vehicle context. Here, agents are designed for specialized tasks—navigation, messaging, media control, and user support—organized using design patterns such as:

• Hierarchical Orchestration : Central agent routes tasks to domain experts.
• Diamond Pattern : Responses are refined post-hoc by moderation agents.
• Peer-to-Peer Handoff : Agents detect misclassification and reroute queries autonomously.
• Collaborative Synthesis : Responses are merged across agents via a Response Mixer.
• Adaptive Looping : Agents iteratively refine results until satisfactory outputs are achieved.

This modular design allows automotive systems to balance low-latency, on-device tasks (e.g., climate control) with more resource-intensive, cloud-based reasoning (e.g., restaurant recommendations).

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Check out the Full Guide here .

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Google Launches Gemini 2.5 Pro I/O: Outperforms GPT-4 in Coding, Supports Native Video Understanding and Leads WebDev Arena

Google Launches Gemini 2.5 Pro I/O: Outperforms GPT-4 Turbo in Coding, Supports Native Video Understanding and Leads WebDev Arena

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Google Launches Gemini 2.5 Pro I/O: Outperforms GPT-4 in Coding, Supports Native Video Understanding and Leads WebDev Arena​

By Asif Razzaq

May 7, 2025

Just ahead of its annual I/O developer conference , Google has released an early preview of Gemini 2.5 Pro (I/O Edition) —a substantial update to its flagship AI model focused on software development and multimodal reasoning and understanding. This latest version delivers marked improvements in coding accuracy, web application generation, and video-based understanding, placing it at the forefront of large model evaluation leaderboards.

With top rankings in LM Arena’s WebDev and Coding categories, Gemini 2.5 Pro I/O emerges as a serious contender in applied AI programming assistance and multimodal intelligence.

Leading in Web App Development: Top of WebDev Arena​

The I/O Edition distinguishes itself in frontend software development, achieving the top spot on the WebDev Arena leaderboard —a benchmark based on human evaluation of generated web applications. Compared to its predecessor, the model improves by +147 Elo points, underscoring meaningful progress in quality and consistency.

Key capabilities include:

• End-to-End Frontend Generation
  Gemini 2.5 Pro I/O generates complete browser-ready applications from a single prompt. Outputs include well-structured HTML, responsive CSS, and functional JavaScript—reducing the need for iterative prompts or post-processing.
• High-Fidelity UI Generation
  The model interprets structured UI prompts with precision, producing readable and modular code components that are suitable for direct deployment or integration into existing codebases.
• Consistency Across Modalities
  Outputs remain consistent across various frontend tasks, enabling developers to use the model for layout prototyping, styling, and even component-level rendering.

This makes Gemini particularly valuable in streamlining frontend workflows, from mockup to functional prototype.

General Coding Performance: Outpacing GPT-4 and Claude 3.7​

Beyond web development, Gemini 2.5 Pro I/O shows strong general-purpose coding capabilities. It now ranks first in LM Arena’s coding benchmark, ahead of competitors such as GPT-4 and Claude 3.7 Sonnet.

Notable enhancements include:

• Multi-Step Programming Support
  The model can perform chained tasks such as code refactoring, optimization, and cross-language translation with increased accuracy.
• Improved Tool Use
  Google reports a reduction in tool-calling errors during internal testing—an important milestone for real-time development scenarios where tool invocation is tightly coupled with model output.
• Structured Instructions via Vertex AI
  In enterprise environments, the model supports structured system instructions, giving teams greater control over execution flow, especially in multi-agent or workflow-based systems.

Together, these improvements make the I/O Edition a more reliable assistant for tasks that go beyond single-function completions—supporting real-world software development practices.

Native Video Understanding and Multimodal Contexts​

In a notable leap toward generalist AI, Gemini 2.5 Pro I/O introduces built-in support for video understanding. The model scores 84.8% on the VideoMME benchmark , indicating robust performance in spatial-temporal reasoning tasks.

Key features include:

• Direct Video-to-Structure Understanding
  Developers can feed video inputs into AI Studio and receive structured outputs—eliminating the need for manual intermediate steps or model switching.
• Unified Multimodal Context Window
  The model accepts extended, multimodal sequences—text, image, and video—within a single context. This simplifies the development of cross-modal workflows where continuity and memory retention are essential.
• Application Readiness
  Video understanding is integrated into AI Studio today, with extended capabilities available through Vertex AI, making the model immediately usable for enterprise-facing tools.

This makes Gemini suitable for a range of new use cases, from video content summarization and instructional QA to dynamic UI adaptation based on video feeds.

Deployment and Integration​

Gemini 2.5 Pro I/O is now available across key Google platforms:

• Google AI Studio : For interactive experimentation and rapid prototyping
• Vertex AI : For enterprise-grade deployment with support for system-level configuration and tool use
• Gemini App : For general access via natural language interfaces

While the model does not yet support fine-tuning, it accepts prompt-based customization and structured input/output, making it adaptable for task-specific pipelines without retraining.

Conclusion​

Gemini 2.5 Pro I/O marks a significant step forward in making large language models practically useful for developers and enterprises alike. Its leadership on both WebDev and coding leaderboards, combined with native support for multimodal input, illustrates Google’s growing emphasis on real-world applicability.

Rather than focusing solely on raw language modeling benchmarks, this release prioritizes functional quality—offering developers structured, accurate, and context-aware outputs across a diverse range of tasks. With Gemini 2.5 Pro I/O, Google continues to shape the future of developer-centric AI systems.

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Check out the Technical details and Try it here .

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LLMs Can Now Talk in Real-Time with Minimal Latency: Chinese Researchers Release LLaMA-Omni2, a Scalable Modular Speech Language Model

LLMs Can Now Talk in Real-Time with Minimal Latency: Chinese Researchers Release LLaMA-Omni2, a Scalable Modular Speech Language Model

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LLMs Can Now Talk in Real-Time with Minimal Latency: Chinese Researchers Release LLaMA-Omni2, a Scalable Modular Speech Language Model​

By Asif Razzaq

May 6, 2025

Researchers at the Institute of Computing Technology, Chinese Academy of Sciences, have introduced LLaMA-Omni2 , a family of speech-capable large language models (SpeechLMs) now available on Hugging Face . This research introduces a modular framework that enables real-time spoken dialogue by integrating speech perception and synthesis with language understanding. Unlike earlier cascaded systems, LLaMA-Omni2 operates in an end-to-end pipeline while retaining modular interpretability and low training cost.

Overview of the LLaMA-Omni2 Architecture​

LLaMA-Omni2 encompasses models ranging from 0.5B to 14B parameters, each built atop the Qwen2.5-Instruct series. The architecture consists of:

• Speech Encoder : Utilizes Whisper-large-v3 to transform input speech into token-level acoustic representations.
• Speech Adapter : Processes encoder outputs using a downsampling layer and a feed-forward network to align with the language model’s input space.
• Core LLM : The Qwen2.5 models serve as the main reasoning engine.
• Streaming TTS Decoder : Converts LLM outputs into speech tokens using an autoregressive Transformer and then generates mel spectrograms through a causal flow matching model inspired by CosyVoice2.

A gating mechanism fuses LLM hidden states with textual embeddings before speech synthesis, enhancing contextual fidelity in the generated audio.

Streaming Generation with Read-Write Scheduling​

The model adopts a read-write strategy to facilitate streaming output. Specifically, for every R tokens produced by the LLM, W speech tokens are generated. This enables synchronized textual and acoustic generation, minimizing latency without compromising fluency.

Empirical findings suggest that setting R = 3 and W = 10 provides a favorable trade-off between latency (~583 ms), alignment (ASR-WER: 3.26), and perceptual quality (UTMOS: 4.19).

Training Approach​

Despite achieving competitive performance, LLaMA-Omni2 is trained on a relatively compact corpus—200K multi-turn speech-to-speech dialogue samples. These samples are synthesized from instruction-following text datasets (Alpaca, UltraChat), with diverse input voices and a consistent output voice generated using FishSpeech and CosyVoice2 models.

Training is executed in two stages:

• Stage I : Independently optimizes the speech-to-text and text-to-speech modules.
• Stage II : Fine-tunes the speech-to-speech generation path, including the gating and autoregressive decoding components.

Benchmark Results​

The models are evaluated on spoken question answering and speech instruction following tasks using both speech-to-text (S2T) and speech-to-speech (S2S) modes.

Model Llama Q (S2S) Web Q (S2S) GPT-4o Score ASR-WER Latency (ms) GLM-4-Voice (9B) 50.7 15.9 4.09 3.48 1562.8 LLaMA-Omni (8B) 49.0 23.7 3.52 3.67 346.7 LLaMA-Omni2-7B 60.7 31.3 4.15 3.26 582.9

The performance scales consistently with model size. Notably, LLaMA-Omni2-14B outperforms all baselines across tasks, even with substantially less training data than native SpeechLMs such as GLM-4-Voice.

Component Analyses​

• Gate Fusion Module : Removing the gating mechanism increases ASR-WER and reduces speech quality, confirming its role in aligning textual and contextual signals.
• TTS Pretraining : Initializing the TTS model from Qwen2.5 and fine-tuning in a streaming setup yields the best performance. Training from scratch fails to converge effectively.
• Read/Write Strategies : Adjusting the R:W ratio impacts latency and quality. Larger W improves UTMOS but at the cost of response delay.

Additionally, the study demonstrates that multi-turn dialogue data is more effective than single-turn data in training speech interaction capabilities, and that performance plateaus around 200K samples.

Conclusion​

LLaMA-Omni2 demonstrates that high-quality, low-latency spoken interaction with LLMs is feasible without the need for extensive pretraining on massive speech corpora. By combining modular architecture with autoregressive streaming synthesis, the system offers a practical pathway for real-time speech applications.

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Check out the Paper , Model on Hugging Face and GitHub Page .

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[bnew]

OpenAI Releases a Strategic Guide for Enterprise AI Adoption: Practical Lessons from the Field

OpenAI Releases a Strategic Guide for Enterprise AI Adoption: Practical Lessons from the Field

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OpenAI Releases a Strategic Guide for Enterprise AI Adoption: Practical Lessons from the Field​

By Asif Razzaq

May 5, 2025

OpenAI has published a comprehensive 24-page document titled AI in the Enterpris e , offering a pragmatic framework for organizations navigating the complexities of large-scale AI deployment. Rather than focusing on abstract theories, the report presents seven implementation strategies based on field-tested insights from collaborations with leading companies including Morgan Stanley, Klarna, Lowe’s, and Mercado Libre.

The document reads less like promotional material and more like an operational guidebook—emphasizing systematic evaluation, infrastructure readiness, and domain-specific integration.

1. Establish a Rigorous Evaluation Process​

The first recommendation is to initiate AI adoption through well-defined evaluations (“evals”) that benchmark model performance against targeted use cases. Morgan Stanley applied this approach by assessing language translation, summarization, and knowledge retrieval in financial advisory contexts. The outcome was measurable: improved document access, reduced search latency, and broader AI adoption among advisors.

Evals not only validate models for deployment but also help refine workflows with empirical feedback loops, enhancing both safety and model alignment.

2. Integrate AI at the Product Layer​

Rather than treating AI as an auxiliary function, the report stresses embedding it directly into user-facing experiences. For instance, Indeed utilized GPT-4o mini to personalize job matching, supplementing recommendations with contextual “why” statements. This increased user engagement and hiring success rates while maintaining cost-efficiency through fine-tuned, token-optimized models.

The key takeaway: model performance alone is insufficient—impact scales when AI is embedded into product logic and tailored to domain-specific needs.

3. Invest Early to Capture Compounding Returns​

Klarna’s early investment in AI yielded substantial gains in operational efficiency. A GPT-powered assistant now handles two-thirds of support chats, reducing resolution times from 11 minutes to 2. The company also reports that 90% of employees are using AI in their workflows, a level of adoption that enables rapid iteration and organizational learning.

This illustrates how early engagement not only improves tooling but accelerates institutional adaptation and compound value capture.

4. Leverage Fine-Tuning for Contextual Precision​

Generic models can deliver strong baselines, but domain adaptation often requires customization. Lowe’s achieved notable improvements in product search relevance by fine-tuning GPT models on their internal product data. The result: a 20% increase in tagging accuracy and a 60% improvement in error detection.

OpenAI highlights this approach as a low-latency pathway to achieve brand consistency, domain fluency, and efficiency across content generation and search tasks.

5. Empower Internal Experts, Not Just Technologists​

BBVA exemplifies a decentralized AI adoption model by enabling non-technical employees to build custom GPT-based tools. In just five months, over 2,900 internal GPTs were created, addressing legal, compliance, and customer service needs without requiring engineering support.

This bottom-up strategy empowers subject-matter experts to iterate directly on their workflows, yielding more relevant solutions and reducing development cycles.

6. Streamline Developer Workflows with Dedicated Platforms​

Engineering bandwidth remains a bottleneck in many organizations. Mercado Libre addressed this by building Verdi , a platform powered by GPT-4o mini, enabling 17,000 developers to prototype and deploy AI applications using natural language interfaces. The system integrates guardrails, APIs, and reusable components—allowing faster, standardized development.

The platform now supports high-value functions such as fraud detection, multilingual translation, and automated content tagging, demonstrating how internal infrastructure can accelerate AI velocity.

7. Automate Deliberately and Systematically​

OpenAI emphasizes setting clear automation targets. Internally, they developed an automation platform that integrates with tools like Gmail to draft support responses and trigger actions. This system now handles hundreds of thousands of tasks monthly, reducing manual workload and enhancing responsiveness.

Their broader vision includes Operator , a browser-agent capable of autonomously interacting with web-based interfaces to complete multi-step processes—signaling a move toward agent-based, API-free automation.

Final Observations​

The report concludes with a central theme: effective AI adoption requires iterative deployment, cross-functional alignment, and a willingness to refine strategies through experimentation. While the examples are enterprise-scale, the core principles—starting with evals, integrating deeply, and customizing with context—are broadly applicable.

Security and data governance are also addressed explicitly. OpenAI reiterates that enterprise data is not used for training, offers SOC 2 and CSA STAR compliance, and provides granular access control for regulated environments.

In an increasingly AI-driven landscape, OpenAI’s guide serves as both a mirror and a map—reflecting current best practices and helping enterprises chart a more structured, sustainable path forward.

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Check out the Full Guide here .

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[bnew]

This AI Paper Introduce WebThinker: A Deep Research Agent that Empowers Large Reasoning Models (LRMs) for Autonomous Search and Report Generation

This AI Paper Introduce WebThinker: A Deep Research Agent that Empowers Large Reasoning Models (LRMs) for Autonomous Search and Report Generation

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This AI Paper Introduce WebThinker: A Deep Research Agent that Empowers Large Reasoning Models (LRMs) for Autonomous Search and Report Generation​

By Sajjad Ansari

May 6, 2025

Large reasoning models (LRMs) have shown impressive capabilities in mathematics, coding, and scientific reasoning. However, they face significant limitations when addressing complex information research needs when relying solely on internal knowledge. These models struggle with conducting thorough web information retrieval and generating accurate scientific reports through multi-step reasoning processes. So, the deep integration of LRM’s reasoning capabilities with web information exploration is a practical demand, initiating a series of deep research initiatives. However, existing open-source deep search agents use RAG techniques with rigid, predefined workflows, restricting LRMs’ ability to explore deeper web information and hindering effective interaction between LRMs and search engines.

LRMs like OpenAI-o1, Qwen-QwQ, and DeepSeek-R1 enhance performance through extended reasoning capabilities. Various strategies have been proposed to achieve advanced reasoning capabilities, including intentional errors in reasoning during training, distilled training data, and reinforcement learning approaches to develop long chain-of-thought abilities. However, these methods are fundamentally limited by their static, parameterized architectures that lack access to external world knowledge. RAG integrates retrieval mechanisms with generative models, enabling access to external knowledge. Recent advances span multiple dimensions, including retrieval necessity, query reformulation, document compression, denoising, and instruction-following.

Researchers from Renmin University of China, BAAI, and Huawei Poisson Lab have proposed a deep research agent called WebThinker that empowers LRMs to autonomously search the web, navigate web pages, and draft research reports during the reasoning process. WebThinker introduces a Deep Web Explorer module that enables LRMs to dynamically search, navigate, and extract information from the web when they encounter knowledge gaps. It employs an Autonomous Think-Search-and-Draft strategy, allowing models to combine reasoning, information gathering, and report writing in real time smoothly. Moreover, an RL-based training strategy is implemented to enhance research tool utilization through iterative online Direct Preference Optimization.

WebThinker framework operates in two primary modes: Problem-Solving Mode and Report Generation Mode. In Problem-Solving Mode, WebThinker addresses complex tasks using the Deep Web Explorer tool, which the LRM can invoke during reasoning. In Report Generation Mode, the LRM autonomously produces detailed reports and employs an assistant LLM to implement report-writing tools. To improve LRMs with research tools via RL, WebThinker generates diverse reasoning trajectories by applying its framework to an extensive set of complex reasoning and report generation datasets, including SuperGPQA, WebWalkerQA, OpenThoughts, NaturalReasoning, NuminaMath, and Glaive. For each query, the initial LRM produces multiple distinct trajectories.

The WebThinker-32B-Base model outperforms prior methods like Search-o1 across all benchmarks on complex problem-solving, with 22.9% improvement on WebWalkerQA and 20.4% on HLE. WebThinker achieves the highest overall score of 8.0, surpassing RAG baselines and advanced deep research systems in scientific report generation tasks, including Gemini-Deep Research (7.9). The adaptability across different LRM backbones is remarkable, with R1-based WebThinker models outperforming direct reasoning and standard RAG baselines. With the DeepSeek-R1-7B backbone, it achieves relative improvements of 174.4% on GAIA and 422.6% on WebWalkerQA compared to direct generation, and 82.9% on GAIA and 161.3% on WebWalkerQA over standard RAG implementations.

In conclusion, researchers introduced WebThinker, which provides LRMs with deep research capabilities, addressing their limitations in knowledge-intensive real-world tasks such as complex reasoning and scientific report generation. The framework enables LRMs to autonomously explore the web and produce comprehensive outputs through continuous reasoning processes. The findings highlight WebThinker’s potential to advance the deep research capabilities of LRMs, creating more powerful intelligent systems capable of addressing complex real-world challenges. Future work includes incorporating multimodal reasoning capabilities, exploring advanced tool learning mechanisms, and investigating GUI-based web exploration.

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Check out the Paper .

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A Coding Guide to Compare Three Stability AI Diffusion Models (v1.5, v2-Base & SD3-Medium) Diffusion Capabilities Side-by-Side in Google Colab Using Gradio

A Coding Guide to Compare Three Stability AI Diffusion Models (v1.5, v2-Base & SD3-Medium) Diffusion Capabilities Side-by-Side in Google Colab Using Gradio

www.marktechpost.com

A Coding Guide to Compare Three Stability AI Diffusion Models (v1.5, v2-Base & SD3-Medium) Diffusion Capabilities Side-by-Side in Google Colab Using Gradio​

By Nikhil

May 5, 2025

In this hands-on tutorial, we’ll unlock the creative potential of Stability AI ’s industry-leading diffusion models, Stable Diffusion v1.5, Stability AI’s v2-base, and the cutting-edge Stable Diffusion 3 Medium , to generate eye-catching imagery. Running entirely in Google Colab with a Gradio interface, we’ll experience side-by-side comparisons of three powerful pipelines, rapid prompt iteration, and seamless GPU-accelerated inference. Whether we’re a marketer looking to elevate our brand’s visual narrative or a developer eager to prototype AI-driven content workflows, this tutorial showcases how Stability AI’s open-source models can be deployed instantly and at no infrastructure cost, allowing you to focus on storytelling, engagement, and driving real-world results.

We install the huggingface_hub library and then import and invoke the notebook_login() function, which prompts you to authenticate your notebook session with your Hugging Face account, allowing you to seamlessly access and manage models, datasets, and other hub resources.

We first force-uninstalls any existing torchvision to clear potential conflicts, then reinstalls torch and torchvision from the CUDA 11.8–compatible PyTorch wheels, and finally upgrades key libraries, diffusers, transformers, accelerate, safetensors, gradio, and pillow, to ensure you have the latest versions for building and running GPU-accelerated generative pipelines and web demos.

We import PyTorch alongside both the Stable Diffusion v1 and v3 pipelines from the Diffusers library, as well as Gradio for building interactive demos. It then checks for CUDA availability and sets the device variable to “cuda” if a GPU is present; otherwise, it falls back to “cpu”, ensuring your models run on the optimal hardware.

We load the Stable Diffusion v1.5 model in half-precision (float16) without the built-in safety checker, transfers it to your selected device (GPU, if available), and then enables attention slicing to reduce peak VRAM usage during image generation.

We load the Stable Diffusion v2 “base” model in 16-bit precision without the default safety filter, transfers it to your chosen device, and activates attention slicing to optimize memory usage during inference.

We pull in Stability AI’s Stable Diffusion 3 “medium” checkpoint in 16-bit precision (skipping the built-in safety checker), transfers it to your selected device, and enables attention slicing to reduce GPU memory usage during generation.

Now, this function runs the same text prompt through all three loaded pipelines (pipe1, pipe2, pipe3) using the specified inference steps and guidance scale, then returns the first image from each, making it perfect for comparing outputs across Stable Diffusion v1.5, v2-base, and v3-medium.

Finally, this Gradio app builds a three-column UI where you can enter a text prompt, adjust inference steps and guidance scale, then generate and display images from SD v1.5, v2-base, and v3-medium side by side. It also features a radio selector, allowing you to select your preferred model output, and displays a simple confirmation message when a choice is made.

A web interface to compare the three Stability AI models’ output

In conclusion, by integrating Stability AI’s state-of-the-art diffusion architectures into an easy-to-use Gradio app, you’ve seen how effortlessly you can prototype, compare, and deploy stunning visuals that resonate on today’s platforms. From A/B-testing creative directions to automating campaign assets at scale, Stability AI provides the performance, flexibility, and vibrant community support to transform your content pipeline.

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Check out the Colab Notebook .

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NVIDIA Open Sources Parakeet TDT 0.6B: Achieving a New Standard for Automatic Speech Recognition ASR and Transcribes an Hour of Audio in One Second

NVIDIA Open Sources Parakeet TDT 0.6B: Achieving a New Standard for Automatic Speech Recognition ASR and Transcribes an Hour of Audio in One Second

www.marktechpost.com

NVIDIA Open Sources Parakeet TDT 0.6B: Achieving a New Standard for Automatic Speech Recognition ASR and Transcribes an Hour of Audio in One Second​

By Asif Razzaq

May 5, 2025

NVIDIA has unveiled Parakeet TDT 0.6B , a state-of-the-art automatic speech recognition (ASR) model that is now fully open-sourced on Hugging Face . With 600 million parameters , a commercially permissive CC-BY-4.0 license , and a staggering real-time factor (RTF) of 3386 , this model sets a new benchmark for performance and accessibility in speech AI.

Blazing Speed and Accuracy​

At the heart of Parakeet TDT 0.6B’s appeal is its unmatched speed and transcription quality . The model can transcribe 60 minutes of audio in just one second , a performance that’s over 50x faster than many existing open ASR models. On Hugging Face’s Open ASR Leaderboard , Parakeet V2 achieves a 6.05% word error rate (WER) —the best-in-class among open models.

This performance represents a significant leap forward for enterprise-grade speech applications, including real-time transcription, voice-based analytics, call center intelligence, and audio content indexing.

Technical Overview​

Parakeet TDT 0.6B builds on a transformer-based architecture fine-tuned with high-quality transcription data and optimized for inference on NVIDIA hardware. Here are the key highlights:

• 600M parameter encoder-decoder model
• Quantized and fused kernels for maximum inference efficiency
• Optimized for TDT (Transducer Decoder Transformer) architecture
• Supports accurate timestamp formatting , numerical formatting , and punctuation restoration
• Pioneers song-to-lyrics transcription , a rare capability in ASR models

The model’s high-speed inference is powered by NVIDIA’s TensorRT and FP8 quantization , enabling it to reach a real-time factor of RTF = 3386 , meaning it processes audio 3386 times faster than real-time .

Benchmark Leadership​

On the Hugging Face Open ASR Leaderboard —a standardized benchmark for evaluating speech models across public datasets—Parakeet TDT 0.6B leads with the lowest WER recorded among open-source models . This positions it well above comparable models like Whisper from OpenAI and other community-driven efforts.

Data based on May 5 2025

This performance makes Parakeet V2 not only a leader in quality but also in deployment readiness for latency-sensitive applications.

Beyond Conventional Transcription​

Parakeet is not just about speed and word error rate. NVIDIA has embedded unique capabilities into the model:

• Song-to-lyrics transcription : Unlocks transcription for sung content, expanding use cases into music indexing and media platforms.
• Numerical and timestamp formatting : Improves readability and usability in structured contexts like meeting notes, legal transcripts, and health records.
• Punctuation restoration : Enhances natural readability for downstream NLP applications.

These features elevate the quality of transcripts and reduce the burden on post-processing or human editing, especially in enterprise-grade deployments.

Strategic Implications​

The release of Parakeet TDT 0.6B represents another step in NVIDIA’s strategic investment in AI infrastructure and open ecosystem leadership . With strong momentum in foundational models (e.g., Nemotron for language and BioNeMo for protein design), NVIDIA is positioning itself as a full-stack AI company—from GPUs to state-of-the-art models.

For the AI developer community, this open release could become the new foundation for building speech interfaces in everything from smart devices and virtual assistants to multimodal AI agents.

Getting Started​

Parakeet TDT 0.6B is available now on Hugging Face , complete with model weights, tokenizer, and inference scripts. It runs optimally on NVIDIA GPUs with TensorRT, but support is also available for CPU environments with reduced throughput.

Whether you’re building transcription services, annotating massive audio datasets, or integrating voice into your product, Parakeet TDT 0.6B offers a compelling open-source alternative to commercial APIs.

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Check out the Model on Hugging Face .

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