Recent LLMs excel at function-level generation, yet falter when a task spans an entire codebase. To close that gap, researchers from Tsinghua University, Shanghai Jiao Tong University and Shanghai AI Lab introduce Code Graph Model (CGM)—a graph-integrated large language model that reasons over whole repositories without relying on tool-calling agents.
How CGM Works
Component
Purpose
Graph Encoder–Adapter
Extracts control-flow, call-graph and dependency edges from every file, converting them into node embeddings.
Graph-Aware Attention
Blends token context with structural edges so the model “sees” long-range relationships across files.
Staged Training
1) text-only warm-up on permissive code; 2) graph-enhanced fine-tuning on 20 K curated repos; 3) instruction tuning for tasks like bug repair and doc generation.
The result is a 72-billion-parameter Mixture-of-Experts checkpoint (CodeFuse-CGM-72B) plus a lighter 13 B variant, both released under Apache 2.0 on Hugging Face.
Benchmark Highlights
Task (RepoBench)
GPT-4o (agent)
DeepSeek-R1
CGM-72B
Bug Fix (pass@1)
62.3 %
55.8 %
64.7 %
Refactor-Large
58.1 %
48.9 %
61.4 %
Doc Generation
71.5 %
66.2 %
72.1 %
CGM matches or beats proprietary agent stacks while running single-shot—no tool chaining, no external memory.
Why It Matters
Agent-Free Reliability – Removes the non-determinism and overhead of multi-call agent frameworks.
Whole-Project Context – Graph attention lets the model track cross-file types, imports and call chains.
Self-Hosted Friendly – Open weights mean enterprises can audit and finetune without data-privacy worries.
Limitations & Roadmap
The authors note performance drops on repos exceeding 50 K lines; future work targets hierarchical graphs and sparse attention to scale further. They also plan IDE plug-ins that stream live graph embeddings to CGM for interactive code assistance.
Takeaway
Code Graph Model shows that marrying graph structure with LLMs can unlock repository-scale intelligence—providing a transparent, open alternative to closed-source agent pipelines for everyday software engineering.
Google has released Gemma 3n, a compact multimodal language model engineered to run entirely offline on resource-constrained hardware. Unlike its larger Gemma-3 cousins, the 3n variant was rebuilt from the ground up for edge deployment, performing vision, audio, video and text reasoning on devices with as little as 2 GB of RAM.
Two Ultra-Efficient Flavors
Variant
Activated Params*
Typical RAM
Claimed Throughput
Target Hardware
E2B
≈ 2 B (per token)
2 GB
30 tokens / s
Entry-level phones, micro-PCs
E4B
≈ 4 B
4 GB
50 tokens / s
Laptops, Jetson-class boards
*Mixture-of-Experts routing keeps only a subset of the full network active, giving E2B speeds comparable to 5 B dense models and E4B performance near 8 B models.
Key Technical Highlights
Native Multimodality – Single checkpoint accepts combined image, audio, video and text inputs and produces grounded text output.
Edge-Optimized Attention – A local–global pattern plus per-layer embedding (PLE) caching slashes KV-cache memory, sustaining 128 K-token context on-device.
Low-Precision Friendly – Ships with Q4_K_M quantization recipes and TensorFlow Lite / MediaPipe build targets for Android, iOS, and Linux SBCs.
Privacy & Latency – All computation stays on the device, eliminating round-trip delays and cloud-data exposure—critical for regulated or offline scenarios.
Early Benchmarks
Task
3n-E2B
3n-E4B
Gemma 3-4B-IT
Llama-3-8B-Instruct
MMLU (few-shot)
60.1
66.7
65.4
68.9
VQAv2 (zero-shot)
57.8
61.2
60.7
58.3
AudioQS (ASR)
14.3 WER
11.6 WER
12.9 WER
17.4 WER
Despite the tiny footprint, Gemma 3n matches or outperforms many 4-8 B dense models across language, vision and audio tasks.
Developer Experience
Open Weights (Apache 2.0) – Available on Hugging Face, Google AI Studio and Android AICore.
Gemma CLI & Vertex AI – Same tooling as larger Gemma 3 models; drop-in replacement for cloud calls when bandwidth or privacy is a concern.
Reference Apps – Google has published demos for offline voice assistants, real-time captioning, and hybrid AR experiences that blend live camera frames with text-based reasoning.
Why It Matters
Unlocks Edge-First Use Cases – Wearables, drones, smart-home hubs and industrial sensors can now run frontier-level AI without the cloud.
Reduces Cost & Carbon – Fewer server cycles and no data egress fees make deployments cheaper and greener.
Strengthens Privacy – Keeping raw sensor data on-device helps meet GDPR, HIPAA and other compliance regimes.
Looking Ahead
Google hints that Gemma 3n is just the first in a “nano-stack” of forthcoming sub-5 B multimodal releases built to scale from Raspberry Pi boards to flagship smartphones. With open weights, generous licences and robust tooling, Gemma 3n sets a new bar for AI everywhere—where power efficiency no longer has to compromise capability.
Google DeepMind Launches AlphaGenome: The AI Breakthrough for DNA Variant Analysis
On June 25, 2025, Google DeepMind announced AlphaGenome, an innovative deep learning model capable of predicting the functional effects of single-nucleotide variants (SNVs) across up to 1 million DNA base pairs in a single pass. Significantly, DeepMind is making the tool available to non-commercial researchers via a preview API, opening doors for rapid genomic discovery.
🔬 Why AlphaGenome Matters
Leverages Long-Range and Base-Resolution Context AlphaGenome processes entire million-base regions, providing both wide genomic context and precise base-level predictions—eliminating the trade-off seen in earlier systems.
Comprehensive Multimodal Outputs It forecasts thousands of molecular properties—including chromatin accessibility, transcription start/end sites, 3D contacts, and RNA splicing—with unparalleled resolution.
Efficient Variant Effect Scoring Users can assess how variants impact gene regulation in under a second by comparing predictions from wild-type vs. mutated sequences.
🧠 Technical Highlights
Hybrid Architecture Combines convolutional layers for motif recognition and transformers for long-distance dependence, inspired by its predecessor, Enformer.
U‑Net Inspired Backbone Efficiently extracts both positional and contact-based representations from full-sequence inputs.
Training & Scale Trained using publicly available consortia data—ENCODE, GTEx, FANTOM5, and 4D Nucleome—covering human and mouse cell types. Notably, training took just four hours on TPUs using half the compute cost of earlier models.
🏆 Performance and Benchmarks
Benchmark Leader Outperforms prior models on 22 of 24 genomic prediction tasks and achieves state-of-the-art results in 24 of 26 variant-effect evaluations.
Disease-Linked Mutation Success Recaptured known mutation mechanisms, such as a non-coding variant in T‑cell acute lymphoblastic leukemia that activates TAL1 via MYB binding.
🔧 Use Cases by the Community
Variant Interpretation in Disease Research A powerful tool for prioritizing mutations linked to disease mechanisms.
Synthetic Biology and Gene Design Helps engineers design regulatory DNA sequences with precise control over gene expression.
Functional Genomics Exploration Fast mapping of regulatory elements across diverse cell types aids in accelerating biological discovery.
⚠️ Limitations & Future Outlook
Not for Clinical or Personal Diagnostics The tool is intended for research use only and isn’t validated for clinical decision-making.
Complex Long-Range Interactions Performance declines on predicting very distant genomic interactions beyond 100,000 base pairs.
DeepMind plans an expanded public release, with broader API access and ongoing development to support additional species and tissue types.
💡 Final Takeaway
AlphaGenome represents a pivotal leap forward in AI-driven genomics: by offering long-sequence, high-resolution variant effect prediction, it empowers researchers with unprecedented speed and scale for exploring the genome’s regulatory code. Its public API preview signals a new frontier in computational biology—bringing deep neural insights directly to labs around the world.
💻 Gemini CLI Places AI Power in Developers’ Terminals
Google has unveiled Gemini CLI, a fully open-source AI agent that brings its latest Gemini 2.5 Pro model directly into developers’ terminals. Built for productivity and versatility, it supports tasks ranging from code generation to content creation, troubleshooting, research, and even image or video generation—all initiated via natural-language prompts.
🚀 Key Features & Capabilities
Powered by Gemini 2.5 Pro: Supports a massive 1 million-token context window, ideal for long-form conversations and deep codebases.
Multi-task Utility: Enables developers to write code, debug, generate documentation, manage tasks, conduct research, and create images/videos using Google’s Imagen and Veo tools.
MCP & Google Search Integration: Offers external context via web search and connects to developer tools using the Model Context Protocol.
Rich Extensibility: Fully open-source (Apache 2.0), enabling community contributions. Ships with MCP support, customizable prompts, and non-interactive scripting for automated workflows.
Generous Free Preview: Personal Google account grants 60 requests/minute and 1,000 requests/day, among the highest rates available from any provider.
🔧 Seamless Setup & Integration
Installs easily on Windows, macOS, and Linux.
Requires only a Google account with a free Gemini Code Assist license.
Works in tandem with Gemini Code Assist for VS Code, providing a unified CLI and IDE experience.
Ideal for both interactive use and automation within scripts or CI/CD pipelines.
Why It Matters
Meets Developers Where They Work: Integrates AI directly into the CLI—developers' most familiar environment—without needing new interfaces.
Long-Context Reasoning: The 1M-token window enables handling large codebases, multi-file logic, and in-depth document analysis in one session.
Multimodal Power: Beyond code, it supports image and video generation—making it a fully-fledged creative tool.
Openness & Community: As open-source software, Gemini CLI invites global collaboration, transparency, and innovation. Google encourages contributions via its GitHub repo
Competitive Edge: With elite token limits and flexibility, it positions itself as a strong alternative to existing tools like GitHub Copilot CLI and Anthropic’s Claude Code
✅ Final Takeaway
Gemini CLI marks a generational leap for developer AI tools—offering open-source freedom, high context capacity, and multimodal capabilities from within the terminal. With generous usage, extensibility, and seamless integration with developer workflows, it emerges as a compelling entry point into AI-first development. For teams and individuals alike, it’s a powerful new way to harness Gemini at scale.
Anthropic Enhances Claude Code with Support for Remote MCP Servers
Anthropic has announced a significant upgrade to Claude Code, enabling seamless integration with remote MCP (Model Context Protocol) servers. This feature empowers developers to access and interact with contextual information from their favorite tools—such as Sentry and Linear—directly within their coding environment, without the need to manage local server infrastructure.
🔗 Streamlined, Integrated Development Experience
With remote MCP support, Claude Code can connect to third-party services hosting MCP servers, enabling developers to:
Fetch real-time context from tools like Sentry (error logs, stack traces) or Linear (project issues, ticket status)
Maintain workflow continuity, reducing context switching between IDE tab and external dashboards
Take actions directly from the terminal, such as triaging issues or reviewing project status
As Tom Moor, Head of Engineering at Linear, explains:
“With structured, real-time context from Linear, Claude Code can pull in issue details and project status—engineers can now stay in flow when moving between planning, writing code, and managing issues. Fewer tabs, less copy-paste. Better software, faster.”
⚙️ Low Maintenance + High Security
Remote MCP integrations offer development teams a hassle-free setup:
Zero local setup, requiring only the vendor’s server URL
Vendors manage scaling, maintenance, and uptime
Built-in OAuth support means no shared API keys—just secure, vendor-hosted access without credential management
🚀 Why This Empowers Dev Teams
Increased Productivity: Uninterrupted workflow with real-time insights, fewer context switches
Fewer Errors: Developers can debug and trace issues precisely without leaving the code editor
Consistency: OAuth integration ensures secure, standardized access across tools
🧭 Getting Started
Remote MCP server support is available now in Claude Code. Developers can explore:
Featured integrations like Sentry and Linear MCP
Official documentation and an MCP directory listing recommended remote servers
✅ Final Takeaway
By enabling remote MCP server integration, Anthropic deepens Claude Code’s role as a next-gen development interface—bringing tool-derived context, security, and actionability into the coding environment. This update brings developers closer to a unified workflow, enhances debugging capabilities, and accelerates productivity with minimal overhead.
Mistral AI has released Mistral Small 3.2, an optimized version of its open-source 24B-parameter multimodal model. This update refines rather than reinvents: it strengthens instruction adherence, improves output consistency, and bolsters function-calling behavior—all while keeping the lightweight, efficient foundations of its predecessor intact.
🎯 Key Refinements in Small 3.2
Accuracy Gains: Instruction-following performance rose from 82.75% to 84.78%—a solid boost in model reliability.
Repetition Reduction: Instances of infinite or repetitive responses dropped nearly twofold (from 2.11% to 1.29%)—ensuring cleaner outputs for real-world prompts.
Enhanced Tool Integration: The function-calling interface has been fine-tuned for frameworks like vLLM, improving tool-use scenarios.
🔬 Benchmark Comparisons
Wildbench v2: Nearly 10-point improvement in performance.
Arena Hard v2: Scores jumped from 19.56% to 43.10%, showcasing substantial gains on challenging tasks.
Coding & Reasoning: Gains on HumanEval Plus (88.99→92.90%) and MBPP Pass@5 (74.63→78.33%), with slight improvements in MMLU Pro and MATH.
Vision benchmarks: Small trade-offs: overall vision score dipped from 81.39 to 81.00, with mixed results across tasks.
MMLU Slight Dip: A minor regression from 80.62% to 80.50%, reflecting nuanced trade-offs .
💡 Why These Updates Matter
Although no architectural changes were made, these improvements focus on polishing the model’s behavior—making it more predictable, compliant, and production-ready. Notably, Small 3.2 still runs smoothly on a single A100 or H100 80GB GPU, with 55GB VRAM needed for full-floating performance—ideal for cost-sensitive deployments.
🚀 Enterprise-Ready Benefits
Stability: Developers targeting real-world applications will appreciate fewer unexpected loops or halts.
Precision: Enhanced prompt fidelity means fewer edge-case failures and cleaner behavioral consistency.
Compatibility: Improved function-calling makes Small 3.2 a dependable choice for agentic workflows and tool-based LLM work.
Accessible: Remains open-source under Apache 2.0, hosted on Hugging Face with support in frameworks like Transformers & vLLM.
EU-Friendly: Backed by Mistral’s Parisian roots and compliance with GDPR/EU AI Act—a plus for European enterprises.
🧭 Final Takeaway
Small 3.2 isn’t about flashy new features—it’s about foundational refinement. Mistral is doubling down on its “efficient excellence” strategy: deliver high performance, open-source flexibility, and reliability on mainstream infrastructure. For developers and businesses looking to harness powerful LLMs without GPU farms or proprietary lock-in, Small 3.2 offers a compelling, polished upgrade.
ReVisual‑R1: A New Open‑Source 7B Multimodal LLM with Deep, Thoughtful Reasoning
Researchers from Tsinghua University, Shanghai Jiao Tong University, and the Shanghai Artificial Intelligence Laboratory have released ReVisual‑R1, a pioneering 7 billion‑parameter multimodal large language model (MLLM) open‑sourced for public use. It offers advanced, context‑rich reasoning across both vision and text—unveiling new possibilities for explainable AI.
🧠 Why ReVisual‑R1 Matters
Training multimodal models to reason—not just perceive—poses a significant challenge. Previous efforts in multimodal chain‑of‑thought (CoT) reasoning were limited by training instability and superficial outputs. ReVisual‑R1 addresses these issues by blending text‑only and multimodal reinforcement learning (RL), yielding deeper and more accurate analysis.
🚀 Innovative Three‑Stage Training Pipeline
Cold‑Start Pretraining (Text Only) Leveraging carefully curated text datasets to build strong reasoning foundations that outperform many zero‑shot models, even before RL is applied.
Multimodal RL with Prioritized Advantage Distillation (PAD) Enhances visual–text reasoning through progressive RL, avoiding gradient stagnation typical in previous GRPO approaches.
Final Text‑Only RL Refinement Further improves reasoning fluency and depth, producing coherent and context‑aware multimodal outputs.
📚 The GRAMMAR Dataset: Key to Quality Reasoning
ReVisual‑R1 is trained on GRAMMAR, a meticulously curated dataset combining text and multimodal data. It offers nuanced reasoning tasks with coherent logic—unlike shallow, noisy alternatives—ensuring the model learns quality thinking patterns.
🏆 Benchmark‑Topping Performance
On nine out of ten benchmarks—including MathVerse, MathVision, WeMath, LogicVista, DynaMath, AIME 2024, and AIME 2025—ReVisual‑R1 outperforms open‑source peers and competes with commercial models, emerging as a top-performing open‑source 7B MLLM.
🔍 What This Means for AI Research
Staged Training Works: Combining text-based pretraining with multimodal RL produces better reasoning than one-step methods.
PAD Innovation: Stabilizes multimodal learning by focusing on high‑quality signals.
Model Accessibility: At 7B parameters and fully open-source, ReVisual‑R1 drives multimodal AI research beyond large-scale labs.
✅ Final Takeaway
ReVisual‑R1 delivers long‑form, image‑grounded reasoning at the open‑source level—transforming the landscape for explainable AI. Its innovative training pipeline, multi-modal fluency, and benchmark dominance make it a new foundation for small, intelligent agents across education, robotics, and data analysis.
MiniMax Unveils Its General AI Agent: “Code Is Cheap, Show Me the Requirement”
MiniMax, a rising innovator in multimodal AI, has officially introduced MiniMax Agent, a general-purpose AI assistant engineered to tackle long-horizon, complex tasks across code, design, media, and more. Unlike narrow or rule-based tools, this agent flexibly dissects task requirements, builds multi-step plans, and executes subtasks autonomously to deliver complete, end-to-end outputs.
Already used internally for nearly two months, the Agent has become an everyday tool for over 50% of MiniMax’s team, supporting both technical and creative workflows with impressive fluency and reliability.
🧠 What MiniMax Agent Can Do
Understand & Summarize Long Documents:
In seconds, it can produce a 15-minute readable summary of dense content like MiniMax's recently released M1 model.
Create Multimedia Learning Content:
From the same prompt, it generates video tutorials with synchronized audio narration—perfect for education or product explainers.
Design Dynamic Front-End Animations:
Developers have already used it to test advanced UI elements in production-ready code.
Build Complete Product Pages Instantly:
In one demo, it generated an interactive Louvre-style web gallery in under 3 minutes.
💡 From Narrow Agent to General Intelligence
MiniMax’s journey began six months ago with a focused prototype: “Today’s Personalized News”, a vertical agent tailored to specific data feeds and workflows. However, the team soon realized the potential for a generalized agent—a true software teammate, not just a chatbot or command runner.
They redesigned it with this north star: if you wouldn’t trust it on your team, it wasn’t ready.
🔧 Key Capabilities
1. Advanced Programming:
Executes complex logic and branching flows
Simulates end-to-end user operations, even testing UI output
Prioritizes visual and UX quality during development
2. Full Multimodal Support:
Understands and generates text, video, images, and audio
Rich media workflows from a single natural language prompt
3. Seamless MCP Integration:
Built natively on MiniMax’s MCP infrastructure
Connects to GitHub, GitLab, Slack, and Figma—enriching context and creative output
🔄 Future Plans: Efficiency and Scalability
Currently, MiniMax Agent orchestrates several distinct models to power its multimodal outputs, which introduces some overhead in compute and latency. The team is actively working to unify and optimize the architecture, aiming to make it more efficient, more affordable, and accessible to a broader user base.
The Agent's trajectory aligns with projections by the IMF, which recently stated that AI could boost global GDP by 0.5% annually from 2025 to 2030. MiniMax intends to contribute meaningfully to this economic leap by turning everyday users into orchestrators of intelligent workflows.
📣 Rethinking Work, Not Just Automation
The blog closes with a twist on a classic developer saying:
“Talk is cheap, show me the code.”
Now, with intelligent agents, MiniMax suggests a new era has arrived:
“Code is cheap. Show me the requirement.”
This shift reframes how we think about productivity, collaboration, and execution in a world where AI can do far more than just respond—it can own, plan, and deliver.
Final Takeaway:
MiniMax Agent is not just a chatbot or dev tool—it’s a full-spectrum AI teammate capable of reasoning, building, designing, and communicating. Whether summarizing scientific papers, building product pages, or composing tutorials with narration, it's designed to help anyone turn abstract requirements into real-world results.
Andrej Karpathy on the Future of Software: The Rise of Software 3.0 and the Agent Era
At a packed AI event, Andrej Karpathy—former Director of AI at Tesla and founding member of OpenAI—delivered a compelling address outlining a tectonic shift in how we write, interact with, and deploy software. “Software is changing again,” Karpathy declared, positioning today’s shift as more radical than anything the industry has seen in 70 years.
From Software 1.0 to 3.0
Karpathy breaks down the evolution of software into three stages:
Software 1.0: Traditional code written explicitly by developers in programming languages like Python or C++.
Software 2.0: Neural networks trained via data and optimized using backpropagation—no explicit code, just learned weights.
Software 3.0: Large Language Models (LLMs) like GPT-4 and Claude, where natural language prompts become the new form of programming.
“We are now programming computers in English,” Karpathy said, highlighting how the interface between humans and machines is becoming increasingly intuitive and accessible.
GitHub, Hugging Face, and the Rise of LLM Ecosystems
Karpathy draws powerful parallels between historical shifts in tooling: GitHub was the hub for Software 1.0; Hugging Face and similar platforms are now becoming the repositories for Software 2.0 and 3.0. Prompting an LLM is no longer just a trick—it’s a paradigm. And increasingly, tools like Cursor and Perplexity represent what he calls partial autonomy apps, with sliding scales of control for the user.
In these apps, humans perform verification while AIs handle generation, and GUIs become crucial for maintaining speed and safety.
AI as Utilities, Fabs, and Operating Systems
Karpathy introduced a powerful metaphor: LLMs as a new form of operating system. Just as Windows or Linux manage memory and processes, LLMs orchestrate knowledge and tasks. He explains that while LLMs operate with the reliability and ubiquity of utilities (like electricity), they also require the massive capex and infrastructure akin to semiconductor fabs.
But the most accurate analogy, he claims, is that LLMs are emerging operating systems, with multimodal abilities, memory management (context windows), and apps running across multiple providers—just like early days of Linux vs. Windows.
Vibe Coding and Natural Language Development
Vibe coding—the concept of programming through intuition and natural language—has exploded, thanks in part to Karpathy’s now-famous tweet. “I can’t program in Swift,” he said, “but I built an iOS app with an LLM in a day.”
The viral idea is about empowerment: anyone who speaks English can now create software. And this unlocks massive creative and economic potential, especially for young developers and non-programmers.
The Next Frontier: Building for AI Agents
Karpathy argues that today’s digital infrastructure was designed for humans and GUIs—not for autonomous agents. He proposes tools like llm.txt (analogous to robots.txt) to make content agent-readable, and praises platforms like Vercel and Stripe that are transitioning documentation and tooling to be LLM-native.
“You can’t just say ‘click this’ anymore,” he explains. Agents need precise, machine-readable instructions—not vague human UX metaphors.
He also showcases tools like Deep Wiki and Ingest to convert GitHub repos into digestible formats for LLMs. In short, we must rethink developer experience not just for humans, but for machine collaborators.
Iron Man Suits, Not Iron Man Robots
Karpathy closes with a compelling analogy: most AI applications today should act more like Iron Man suits (human-augmented intelligence) rather than fully autonomous Iron Man robots. We need GUIs for oversight, autonomy sliders to control risk, and workflows that let humans verify, adjust, and approve AI suggestions in tight loops.
“It’s not about replacing developers,” he emphasizes. “It’s about rewriting the stack, building intelligent tools, and creating software that collaborates with us.”
Takeaway:
The future of software isn’t just about writing better code. It’s about redefining what code is, who gets to write it, and how machines will interact with the web. Whether you’re a developer, founder, or student, learning to work with and build for LLMs isn’t optional—it’s the next operating system of the world.
