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Like Granola, but for healthcare. Fully HIPAA-compliant. Discussion | Link

He's 13 years old. He wanted to track his own ant colonies — growth, feeding, humidity, and other metrics. He built the whole app himself with some help from AI tools; I just helped him deploy it to a server. Would love to hear your feedback!

But the company is still claiming that New Glenn will return to flight this year.

Much of it comes from heavy-duty trucks and buses that burn diesel.

GitHub热门项目 | Instatic is a modern self-hosted visual CMS - get it running in 1 minute | Stars: 1,346 | 351 stars today | 语言: TypeScript

To this day, we have yet to see a quantum computer conclusively perform a single useful task. Existing machines are simply too small and error-ridden to solve commercially relevant problems. That hasn't stopped Donald Trump's science adviser from promising a "quantum computer powerful enough for scientific discovery by 2028" and Trump from issuing a new […]

Last week we introduced Upsun Dispatch™: a platform for the agentic software development lifecycle, where workflow is the primitive , not the agent. Today, prerelease is open. Starting July 1, a founding cohort of design partners gets early access. This is not a traditional beta. We are looking for engineering teams already running AI workflows and hitting the limits of what individual tools can do: teams who want to help shape what Upsun Dispatch becomes, not just use what we ship. Design partners get direct access to the core team, real input on the roadmap, and charter terms that reflect a genuine partnership rather than a vendor relationship. Apply at upsun.com/dispatch . Read the full announcement: Upsun Dispatch™ is now open for prerelease Starting July 1, 2026, Upsun Dispatch™ is open to a founding cohort of design partners. Here is what joining early means and how to apply upsun.com

If your Terraform plans are slow, your blast radius is too wide, or multiple teams are stepping on each other's changes, it's time to split your monolith. See The Problem with Large Terraform States for how to diagnose whether you've reached that point. This guide walks through the process of breaking a monolithic Terraform state into smaller, focused states — and how Snap CD can manage the dependencies between them so you don't have to. The approach 1. Identify natural boundaries Look at your resources and group them by lifecycle and ownership. Common boundaries: Networking — VPCs, subnets, route tables, NAT gateways. Changes rarely, underpins everything. DNS — Zones, records. Usually owned by a platform team. Compute — Kubernetes clusters, VM scale sets, container services. Changes more often, depends on networking. Application infrastructure — Databases, caches, queues, storage accounts. Owned by application teams. Monitoring — Dashboards, alerts, log sinks. Changes frequently, depends on everything but nothing depends on it. A useful test: if two resources would never be changed in the same PR by the same person, they probably belong in different states. 2. Map the dependencies Before you move anything, draw the dependency graph. Which groups produce values that other groups consume? networking dns │ ▲ ▼ │ compute ──────────►─┘ │ ▼ application │ ▼ monitoring The outputs that cross these boundaries are what you'll need to wire up after the split. Typical examples: Networking → Compute: vpc_id , private_subnet_ids Compute → DNS: load_balancer_ip Compute → Application: cluster_endpoint , cluster_ca_certificate Application → Monitoring: database_id , cache_name 3. Use terraform state mv to migrate resources Terraform's state mv command lets you move resources from one state to another without destroying and recreating them. # Initialize the destination state cd modules/networking terraform init # Move resources from the monolith to the new state terraform state mv \

Sources suggest Musk may be mulling big donation to Trump Accounts.

At some point every growing Terraform project hits a wall. Plans that used to finish in seconds now take minutes. Applies feel risky because hundreds of resources share a single blast radius. Colleagues avoid running terraform plan because it hammers cloud APIs hard enough to trigger throttling. The state file itself becomes a liability — large, slow to lock, and one bad write away from corruption. This guide covers the symptoms of an oversized state, the band-aids teams reach for, and the structural fix that actually works. How Terraform state works under the hood Every terraform plan does two things: Refresh — for every resource in state, Terraform calls the provider's API to read the current real-world status. A state with 500 resources means 500+ API calls, often more when resources have nested data sources. Diff — compare the refreshed state against the desired configuration and produce a change set. The refresh phase is the bottleneck. It's sequential per provider (parallelism helps across providers, not within one), and every resource pays the cost whether you changed it or not. Adding ten resources to a 500-resource state doesn't make plans 2% slower — it makes the refresh 2% slower on every single plan, for every engineer, forever. Symptoms of a state that's too large Slow plans The most visible symptom. Plan time scales with resource count because every resource is refreshed on every plan, regardless of whether its configuration changed. The exact speed depends on provider — AWS resources with complex nested structures (IAM policies, security group rules) are slower to refresh than simple ones, and Azure resources that require multiple API calls per refresh are worse still. These aren't edge cases — users regularly report 2,900-resource states taking 20–25 minutes to plan and 1,600-resource states taking 8+ minutes . Even starting Terraform with a large state can take minutes before a single API call is made . There's a long-standing proposal for terraform