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The White House is easing restrictions on Anthropic’s most advanced AI models weeks after ordering the company to suspend access for foreign nationals.

One of the things I love most about the tech community is that there isn't a single path to becoming...

Dish, the company that operates Dish TV and Sling TV, has filed for Chapter 11 bankruptcy," as reported earlier by Reuters. The plan will allow the EchoStar-owned company to continue to wind down its wireless operations after "unforeseen delays" held back its sale of $23 billion worth of 5G spectrum to AT&T. Dish TV, Sling […]

Also, the science of poop's distinctive shape, boron buckyballs, and the secret to a soccer feint.

Today I noticed the inclusion of emojis in Craigslist's listings/categories: https://www.craigslist.org/area/sfbay . Now, Craigslist, as a legacy of the 1990s web, has for a long time stubbornly maintained its minimalist style, to the point where several "modern" startups have popped up to try and offer Craigslist-like services to new generations. So why this change? And what's with the timing? It's coinciding with the wanton proliferation of emojis everywhere courtesy of everyone's favorite GPT

Most "agent memory" and "agent context" tools today require sending your data to someone else's cloud. If you operate in a regulated, air-gapped, or simply privacy-conscious environment, that rules them out before you've even tried them. I build the opposite: two MIT-licensed, local-first MCP servers that do this work entirely on your own hardware. The problem Agent memory and context assembly are converging on a cloud-only default. That's a non-starter for defense, healthcare, finance, legal, and any team that can't or won't let agent context leave their VPC. It's also just slower and less deterministic than it needs to be: agents re-discover the same facts about your repo and services every session, burning tokens and turns before doing any real work. Mimir: persistent memory, fully offline Mimir is a single ~8MB Rust binary. It encrypts everything at rest with AES-256-GCM, and it works with no API key, no model download, and no network access at all, because the embeddings used for dense search are bundled directly into the binary. It's bi-temporal: every fact carries a validity window, so you can query memory "as of" any past point and supersede facts without deleting history. 43 MCP tools, SQLite + FTS5 hybrid search under the hood. One honest tradeoff worth naming: the FTS5 index needed for fast keyword search currently sits over plaintext, even though the underlying record is encrypted at rest. We're upfront about this in the docs rather than overstating the encryption story. Perseus: compile-before-context Perseus takes a different approach to context than runtime tool-call discovery. Instead of letting an agent rediscover your git state, running services, and test status through a chain of tool calls every session, it compiles all of that into a ready briefing the moment a session starts. The result is deterministic and byte-stable: the same repo state always produces the same compiled context. Honest, reproducible benchmarks On paraphrased queries, Mimir's

The free open source agentic program is finally invading your phone.

3 platforms, one queue, zero editorial control. That was the state of my content automation before I sat down to spec the dashboard. LinkedIn, X, and Threads each had their own generator, their own state files, their own publishing loop. Drafts got generated, passed a quality gate, and fired into the void. If the draft was mediocre or the timing was wrong, I found out after the fact. The problem is not the automation. Automation is why I can run three platform engines without spending two hours a day managing content. The problem is that zero editorial visibility means you cannot catch the bad ones before they post. What I wanted: see every draft before it goes out. Edit inline if needed. Post immediately or schedule for the next slot. Compose something manually when I have a specific take to push. Keep the comment automation untouched because that runs high frequency, low stakes, and babysitting individual replies defeats the point. The spec came out to three core flows. Review queue. Every pregenerated draft surfaces here with full context: platform, topic, generation timestamp, quality score. One click to edit inline, one to approve for the next slot, one to post immediately. The goal is a 30 second review per draft, not a full editing session. Manual compose. Sometimes I know exactly what I want to say. A text area, platform selector, and post button. No generation, no queue, just publish. This is the escape hatch for when something is happening in real time and the pregenerated queue is irrelevant. Schedule view. A simple calendar showing what is queued for which slot across all three platforms. The generator already handles slot logic and quiet hours. The dashboard just needs to surface the state so I can see gaps and move things around without touching JSON files directly. What I deliberately left out: comment automation. That pipeline runs separately, fires frequently, and does not benefit from human review on every reply. Adding it to the dashboard would cr

AWS ECR Guide for ECS Fargate Teams Originally published at https://fortem.dev/blog/aws-ecr-guide AWS ECR from the ECS Fargate operator's seat: how pulls work, the execution-role IAM, why private-subnet tasks fail, real pricing, and the lifecycle policy that cuts the bill. Every ECS Fargate deploy pulls an image from ECR — and ECR is the part nobody owns until it breaks. A task in a private subnet throws ResourceInitializationError , or five years of untagged images quietly push the bill to $400/month. This is ECR from the ECS operator's seat: how pulls actually work, the IAM the execution role needs, what it costs at fleet scale, and the lifecycle, scanning, and replication settings that matter at 10+ environments — with the AWS-verified pricing nobody else itemizes. TL;DR ECR is AWS's managed container registry — the default image store for ECS and EKS. Registry → repository → image, with IAM-based access and a short-lived auth token per pull. The #1 ECR failure on Fargate is a private-subnet task that can't pull: it needs either a NAT gateway or three ECR VPC endpoints, plus AmazonECSTaskExecutionRolePolicy on the execution role. ECR storage is $0.10/GB-month; same-region pulls to Fargate are free. The hidden bill is old images — one team went from $400/mo to ~$15/mo with a 30-day lifecycle policy. At fleet scale three settings matter: lifecycle policies (cost), scan-on-push (security), and cross-account replication (multi-account image distribution). For ECR-heavy fleets in private subnets, VPC interface endpoints are often cheaper than routing every pull through a NAT gateway. Ready to use — copy this today Push an image, then a lifecycle policy that keeps the bill flat, then the exact networking + IAM a private-subnet Fargate task needs to pull: # 1. Authenticate Docker to your private ECR registry, then push aws ecr get-login-password --region us-east-1 \ | docker login --username AWS --password-stdin \ 123456789012.dkr.ecr.us-east-1.amazonaws.com docker tag

"Privacy by design" is one of those phrases you read everywhere and rarely understand. It is often treated as a document to attach to a project, a box to tick before going live. In reality it is not a piece of paperwork: it is the way software is conceived and built from the very first line, so that it protects people's data without anyone having to remember to do so afterwards. What the GDPR actually says The principle is written plainly in Article 25 of the GDPR, which speaks of "data protection by design and by default". These are two distinct things. Protection by design concerns the choices made while the system is being built. Protection by default concerns how the system behaves the moment it is switched on, before anyone touches a single setting. The law does not mandate a specific technology. It asks for an outcome: that data protection be built into the system, proportionate to the risks, and not bolted on afterwards as a patch. It is a difference of substance, not of form. A well-designed system does not have to chase compliance: it already has it inside. It is not a document, it is an architecture The most common mistake is to reduce privacy by design to a file. A report is written, filed, and the building goes on exactly as before. But a PDF protects no data. What protects data are the technical decisions: what information is collected, where it is stored, who can see it, how long it stays, what happens when it is no longer needed. These decisions are made at design time, and changing them later costs far more than getting them right at the start. The principles, turned into concrete choices Privacy by design becomes useful only when it stops being a slogan and turns into a series of choices. Translated into practice, the principles sound like this. Minimisation. You collect only the data genuinely needed to deliver the service. A field you do not collect does not need protecting, cannot be lost in a breach, does not need keeping. The safest piece of da