Thomas Dohmke, who stepped down as GitHub’s CEO last year to become a founder again, is opening a preview of a distributed Git network on Wednesday that is designed to keep fleets of AI coding agents from overwhelming a single central server — and one that may soon compete directly with GitHub’s core service.

Entire, Dohmke’s post-GitHub startup, is launching a preview of this on Wednesday (but for now, it is behind a waitlist). With this, developers can mirror an existing GitHub repository onto Entire’s own infrastructure in one step.

“In the era of agents, centralized Git hosting has become a fundamental constraint, as the strain of billions of agents and developers hammering a central server shows up in the form of rate limits, high latency, or even outages,” says Dohmke in today’s announcement. “Today, we begin to return Git to its original promise, with a distributed, and soon fully decentralized and open-source network of interconnected nodes around the world. By doing so, we enable any developer or agent to host their code in-region, pushing, pulling, and cloning close to where they operate, fast and without bottlenecks, while still part of a global, collaborative network.”

The key here is that the code stays on GitHub, as Entire stresses, but coding agents can work with the Entire mirror and, as the company notes, “build without rate limits.”

Entire’s mirror is meant to absorb the constant flow of traffic that a fleet of agents can generate. That traffic, after all, is part of the reason GitHub is often buckling under pressure these days and startups like Entire have an opening.

Centralized Git hosting, Dohmke says in an interview with The New Stack, has become “a fundamental constraint” now that billions of agent and developer operations land on the same servers, showing up as rate limits, latency, and outages.

Given GitHub’s recent availability issues, it’s no surprise that startups are trying to get into this space. Entire is one — and it has the pedigree — but in June, Cursor also announced Origin, its own Git forge rebuilt for swarms of agents that are cloning and committing against a single repository in parallel.

Entire is starting with active regions in the United States, the European Union, and Australia, but the team says that now that is has spun up its first few regions, it will add more soon.

‘Git as a database’

To build its network, Entire rewrote the server part of git. GitHub, GitLab, and Bitbucket all wrap the server-side of the Git binary and build their infrastructure around it. Entire started from scratch.

“We see Git really as a database,” Dohmke says. The open source Git project has two halves, he explains: the client that an agent uses to talk to a repository, and the server a host runs to manage storage. Rather than build on that stock server, like most companies would do, “we made the decision of not going that route, and instead implemented our own Git backend.”

That only makes sense if Entire’s version has significantly better performance than the stock Git server, of course. Entire says its benchmarks have pushed the network to a sustained rate of 570,000 clones per hour, 586 pushes per second, and roughly 470 combined clone-and-push operations per second.

Pushing to a native Entire branch can run up to 25 times faster than pushing through to GitHub, Dohmke says.

Entire it will open-source both the git backend and the benchmark suite.

The foundation layer, now real

When Dohmke first described Entire’s plans to The New Stack in February, he described a three-layer platform that included a Git-compatible database at the bottom, a semantic reasoning layer in the middle, and an interface on top. Even then, he said that the database, unlike a centralized Git host, could be a globally distributed network of nodes.

But in February, Dohmke also said Entire wouldn’t necessarily end up competing with GitHub, and that code repositories would stay central to the pitch.

Pressed on whether that still holds now that Entire hosts its own copy of the GitHub repo, he calls the mirror complementary, in part because Entire can offer enterprises the ability to keep their code in a local region to fulfill local regulations. He also notes that GitHub has a huge ecosystem and an extended feature set.

“I think the question for the buyer really is, is it not better for me from an availability and reliability perspective, that I have both of these products, so if one of them is down — there’s always going to be single points of failure and human errors — then I have my mirror on the other side,” he says. “But we certainly will, in deals, compete for the dollar spent at a much smaller scale compared to the multi-billion-dollar business that is GitHub today.”

For now, that keeps the two complementary. Dohmke argues that GitHub remains the “source of truth,” or “cold storage,” while the working copy lives on Entire. But he also says that Entire will launch native repositories in the coming months, and those wouldn’t need GitHub underneath at all. All of this will be open-sourced as well.

Entire raised its $60 million seed round in February, when it had 15 employees. Felicis led it, with Microsoft’s venture arm among the backers. The company is now past 40 people and aiming for 60 by the end of the year.

Entire beyond Git: the semantic memory layer

Entire is building its middle layer — the semantic reasoning layer — in parallel with the Git platform.

The semantic layer now integrates with every major coding agent, including Claude Code, Codex, Cursor, Factory AI, and GitHub Copilot, and records each session, prompt, and tool call in the repository alongside the code.

Having this data is useful for agents, and it was the first core service the company launched. Now, it is also building more services on top of that history.

The company is adding Entire Blame, for example, which shows not just who last touched a line but the agent session and prompt behind it. There is also Entire Review, which fans out several agents for an intent-aware review, and the company is adding a code and semantic search feature that lets agents (and developers) search across code changes and the reasoning that produced them.

“Session logs are now the second most important artifact in software development, and they belong in the repository alongside the code,” Dohmke says.

The post Entire is building a Git network for agents appeared first on The New Stack.

Industrial machinery generates more alarms than technicians can triage. For each important alarm requiring follow-up, the technician pulls historical context,...

Industrial machinery generates more alarms than technicians can triage. For each important alarm requiring follow-up, the technician pulls historical context, determines the correct procedure, checks whether a specialist signal confirms the failure mode, and writes up a recommendation. This process remains consistent, and is well-suited for an AI agent. This post discusses a per-alarm…

Source

Last week AWS Summit New York City brought together thousands of customers, partners, and builders for a free, one-day event showcasing the latest in cloud and AI innovation. Dr. Swami Sivasubramanian, VP of Agentic AI at AWS unveiled a stack of AI launches in his keynote, all built around one thesis: agents that compound value over time.

• Agents for working – You can launch autonomous agents and access a smarter activity feed with new Amazon Quick features, which now let you create and run multi-step agents directly in the desktop app and consolidates email, Slack, calendar, and tasks into a single prioritized view with personalized rules.
• Agents for securing – You can shift from reactive to proactive security with AWS Continuum, a new AI-native security service that reasons, validates, and acts at machine speed across the full code vulnerability lifecycle. AWS Security Agent (now part of AWS Continuum) adds new features: threat modeling; pull request code scanning with remediation across major Git platforms; and IDE integrations via Kiro power, Claude Code plugin, and MCP.
• Agents for building – You can write, ship, and modernize code in one continuous loop with Kiro, AWS DevOps Agent, and AWS Transform. Kiro introduces a native iOS app; AWS DevOps Agent adds release management capabilities to assess code changes before production; and AWS Transform continuous modernization reduces tech debt autonomously.
• Agents customers create – You can go from agent idea to production in minutes with Amazon Bedrock AgentCore, which now includes a GA harness for infrastructure and orchestration, Web Search, Managed Knowledge Base, policy integrations with Guardrails, and the new AWS Context service for mapping organizational data relationships.

To learn more, visit the Summit recap from our top announcements blog post and Amazon News post.

Last week’s launches
Here are last week’s launches that caught my attention:

• AWS Local Zone in Hanoi, Vietnam – This new Local Zone is one of the first AWS Local Zones in the Asia Pacific with support for Amazon S3 and Amazon EBS Local Snapshots, enabling customers to meet data residency requirements by storing and backing up data locally. To get started, enable the Hanoi Local Zone (ap-southeast-1-han-1a) from the Regions and Zones tab in the AWS Global View or by using the ModifyAvailabilityZoneGroup API.
• AWS Blocks, an open-source TypeScript framework for application developers (preview) – AWS Blocks runs a fully functional local environment with Postgres, authentication, and real-time messaging, no AWS account required. When you’re ready to deploy, the same application code runs on production AWS services with zero changes, and you can drop into AWS CDK at any point for direct resource configuration.
• Grok 4.3 from xAI in Amazon Bedrock – You can use the Grok 4.3 model on Amazon Bedrock, giving you even more choice as you build generative AI applications across reasoning, agentic, and enterprise workflows. Grok 4.3 runs on a new inference engine in Bedrock designed for price performance, with support for tool calling, structured output, and response streaming.
• Amazon S3 annotations: attach rich, queryable context directly to your objects – Amazon S3 now lets you attach up to 1 GB of rich, mutable, and queryable context directly to your objects using annotations, purpose-built for AI agents and autonomous workflows that need to discover, understand, and act on data at scale without maintaining separate metadata systems.
• Amazon ECS announces faster service auto scaling – Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster).
• Amazon EC2 G7 instances accelerated by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs – AWS is the first major cloud provider to support NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. G7 instances are accelerated by these GPUs with custom sixth-generation Intel Xeon Scalable processors, delivering up to 4.6x AI inference performance and up to 2.1x graphics performance compared to G6 instances.
• Strands Agents introduces new capabilities – Strands is an open source toolkit for building production agents. You can now use better context management in Harness SDK, a new isolated execution environment with Strands Shell, and chaos testing and red teaming in Strands Evals.
• AWS Management Console Private Access – You can access the AWS Console from VPCs without internet connectivity, allowing enterprises to manage their AWS infrastructure through the console while maintaining strict network security controls in air-gapped environments.
• AWS Marketplace Storefront is now generally available – AWS Partners can create and deploy their own branded catalog of solutions and services on their website or application in hours. Channel Partners and Independent Software Vendors can now simplify how they manage their cloud marketplace business and make it easier for customers to discover and purchase their solutions from AWS Marketplace.
• Palo Alto Networks (PANW) Advanced DNS Security on Amazon Route 53 Resolver DNS Firewall (preview) – You can now enforce DNS threat protections from Palo Alto Networks directly on Route 53 DNS Firewall rules, without deploying separate firewalls or modifying VPC configurations — by subscribing to PANW from the DNS Firewall console through the embedded AWS Marketplace widget.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Price reductions 
AWS continues to look for ways to increase performance and lower prices for our customers. I noticed a few such efforts last week, so I’d like to share them:

• Amazon S3 Vectors reduces query charges by up to 80% for large vector indexes – This reduction lowers costs for customers running similarity search across large-scale AI, RAG, and semantic search workloads. The new pricing applies automatically with no application changes required.
• Amazon GameLift Servers introduces free network bandwidth – Amazon GameLift Servers provides network bandwidth in and out of AWS at no additional charge for all instance types from generation 6 and later, including On-Demand and Spot, with no commitment required. You now pay only for your Amazon GameLift Servers instance hours; all network bandwidth is free.
• AWS Marketplace reduces listing fee for professional services to 0.5% from 2.5% – This reduction makes it more cost-effective for consulting partners, systems integrators, managed services providers and independent software vendors to transact their services through AWS Marketplace, while retaining the procurement and billing benefits that come with it.

Learn more about AWS, browse and join upcoming AWS-led in-person and virtual events, startup events, and developer-focused events as well as AWS Summits and AWS Community Days. Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Channy

Today at the AWS Summit in New York City, Swami Sivasubramanian, AWS VP of Agentic AI, provided the day’s keynote.

Here’s our roundup of the biggest announcements from the event:

New in agents customers create
We’re introducing new capabilities on Amazon Bedrock AgentCore: connecting AI agents to organizational, web, and paid knowledge, helping teams find and fix what’s going wrong in production, and enforcing controls that scale as agents grow more capable.

Together, these capabilities help you build more capable agents faster, govern those agents with controls that scale, and improve them continuously. To learn more, read our blog post covering all the new features.

• Introducing Amazon Bedrock Managed Knowledge Base for faster, more accurate enterprise AI applications – You can build enterprise RAG pipelines with the managed Knowledge Base on Bedrock. It provides native data connectors, Smart Parsing for automatic multi-format data preparation, and an Agentic Retriever for complex multi-step queries, all integrated with AgentCore Gateway so developers can focus on business outcomes rather than infrastructure management.
• Announcing Web Search on Amazon Bedrock AgentCore: Ground your AI agents in current, accurate web knowledge – You can use a fully managed web search tool that enables agents to ground responses in current, cited web knowledge with zero data egress from customer’ secured AWS environment. You can focus on building agents instead of manually adding web search to agents on Bedrock AgentCore and managing its infrastructure.
• AWS WAF adds AI traffic monetization capability to help content owners charge AI bots for content access – You can use a new Bot Control capability that enables content providers and publishers price, meter, and collect payment from AI bots and agents accessing their content and APIs. AWS WAF now lets you set a price for that access, accept payment through third-party providers, and grant scoped access directly at the edge.
• Amazon Bedrock AgentCore harness in now generally available – You can do building and running production-grade AI agents in minutes, without coding orchestration loops, by defining your agent’s model, tools, skills, and instructions in configuration, with Bedrock AgentCore harness.
• Coming soon: AWS Context – This is a new service that automatically maps the relationships across your existing data into a knowledge graph and provides agentic search so AI agents in the organization can access governed data relationships, business rules, and domain knowledge at runtime.

New in agents for securing

• Introducing AWS Continuum: Security at machine speed – AWS Continuum for code vulnerabilities, available in a gated preview, takes findings from across your environment, prioritizes by business impact, proves which are exploitable, and drives a fix through your own process.
• AWS Security Agent (now part of AWS Continuum) adds threat modeling, Kiro power and Claude Code plugin, and more – You can generate the new threat modeling (preview) to understand the full context of your application and identify threats with recommended mitigations using the STRIDE framework. You can also use pull request code scanning with remediation across major Git platforms, and IDE integrations via Kiro power, Claude Code plugin, and MCP, letting developers run security reviews and fix issues without context switching.

New in agents for building

• Introducing Kiro for iOS – Kiro introduces a native iOS app, available in a gated preview, built for real engineering work that gives developers a new surface to kick off, monitor, steer, and interact with their Kiro sessions directly from their phone. That means you can now start sessions, check back when they’re done, review diffs, and approve changes all while staying connected to your work with no laptop running.
• AWS DevOps Agent adds release management capabilities to assess code changes before production – You can use a new release readiness review of code changes and autonomous release testing. These new features verify every change against the natural language standards you give to the DevOps Agent and run change-specific tests in production-like environments.
• Proactively reduce tech debt autonomously with AWS Transform – continuous modernization – You can use continuous analysis (preview) to automatically scan your code repositories against configurable baselines and generates findings in hours, not weeks. Once you’ve identified and prioritized findings, you can configure autonomous remediations that generate pull requests for affected repositories automatically.

New in agents for works

With the launch of Amazon Quick’s new autonomous agents, you can create agents that work in the background with specific expertise, tone, and access to tools. You can create a finance agent to process orders as they come in, or a sales agent monitoring interactions across your CRM, emails, and Slacks to proactively draft follow-ups, flag risks, or recommend next steps.

We are also releasing a new activity feed that is tailored to how you work. It consolidates email, messaging, calendar, and tasks into a single prioritized view, learns which messages you always answer fast, which threads you skip, and what topics drive your week.

To learn more, look the demo of Amazon Quick – AI Assistant.

In addition to the keynote announcements, we have other important launches this week:

• Amazon S3 annotations: attach rich, queryable context directly to your objects – Amazon S3 now lets you attach up to 1 GB of rich, mutable, and queryable context directly to your objects using annotations, purpose-built for AI agents and autonomous workflows that need to discover, understand, and act on data at scale without maintaining separate metadata systems.
• Amazon ECS announces faster service auto scaling – Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster, 4.2x), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster, 3.5x).
• Amazon EC2 G7 instances accelerated by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs – AWS is the first major cloud provider to support NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. G7 instances are accelerated by these GPUs with custom sixth-generation Intel Xeon Scalable processors, delivering up to 4.6x AI inference performance and up to 2.1x graphics performance compared to G6 instances.
• Strands Agents introduces new capabilities – Strands is an open source toolkit for building production agents. You can use now better context management in the Harness SDK, a new isolated execution environment with Strands Shell, and chaos testing and red teaming in Strands Evals.

Updated on June 18, 2026 — Added new important launches on June 18.

From Rosie the Robot to Real Conversations

Remember Rosie the Robot from The Jetsons? She wasn’t just a housekeeper. She was a glimpse into a future where technology could hold a conversation, help with everyday tasks, and feel almost part of the family.

Rosie could handle her routines with ease, but the real promise was what she represented: a world where machines could engage in authentic dialogue and adapt both the conversation and the tasks they perform to meet the moment.

In many ways, customer service AI has been chasing that vision. But most solutions are still stuck in the early stages.