标签：#c

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

At an event for pharmaceutical executives, biotech founders, and researchers on Tuesday, Anthropic announced Claude Science, a major new product intended to support scientific research in the same way that Claude Code supports software engineering. Like Claude Code, Claude Science can autonomously carry out meaningful work when given concise, high-level instructions, and it has access…

If you've ever done a timed C++ coding assessment on a platform like HackerRank or DevSkiller, you know the friction isn't really the algorithm — it's the loop . Download a zip with a weird filename, unzip it, hunt for the project root, configure CMake, build, run GTest, fix one failing test, repeat... and somewhere in there you've burned ten minutes of your one-hour window just fighting the harness instead of writing code. These platforms' in-browser editors are fine for quick problems, but for anything involving multiple files (headers, sources, a real test suite), I'd rather work in my own terminal and editor. The catch is that you still have to get the project out of the browser sandbox, build it locally with the exact same toolchain (CMake + GTest), and then package it back up in a way the grader will accept. So I wrote three small bash scripts to remove that friction entirely. Sharing them here in case they save someone else the same ten minutes. The workflow Download the project archive from the platform (zip or tar.gz, filename is whatever the platform gives you — often randomized) Extract it — script 1 handles this regardless of filename or archive type Iterate — script 2 configures CMake once, then repeatedly builds and runs GTest, optionally watching for file changes Package — script 3 strips build artifacts and any local helper scripts, then zips it back up under a name that won't collide with the original download, ready to re-upload Script 1: extract_and_setup.sh Most of these platforms hand you an archive with an unpredictable filename. This script extracts whatever you point it at ( .tar , .tgz , .tar.gz , or .zip ), figures out which directory it unpacked to by diffing the folder listing before and after, and drops the build script into it automatically. #!/usr/bin/env bash # extract_and_setup.sh # Extracts $fname (tar, tgz, tar.gz, or zip) into the CURRENT folder, # then copies run_build.sh into the directory that was created. # # Usage: # ./extrac

Data Modeling, Relationships, and Schemas in Data Analytics In the fields of data analytics, data warehousing, and database management, modeling and schema design are the fundamental pillars used to organize and query information efficiently. This article provides a comprehensive guide to these core concepts. 1. Data Modeling Data modeling is the architectural process of designing how data is stored, interconnected, and accessed within a system. Core Questions Addressed: Storage: What specific data points need to be captured? Structure: How should individual tables be organized? Connectivity: How do these tables interact with one another? Levels of Data Models: Conceptual Model: A high-level business perspective focusing on entities and their relationships, devoid of technical specifications. Logical Model: Defines specific attributes, keys, and relationships. It is independent of the Database Management System (DBMS). Physical Model: The actual implementation within a database, including technical details like indexes, partitions, and storage requirements. 2. Relationships Relationships define the logic of how data in one table corresponds to data in another. One-to-One (1:1): A single record in Table A relates to exactly one record in Table B. One-to-Many (1:M): The most common relationship; for example, one Customer can place many Orders . Many-to-Many (M:M): Multiple records in one table relate to multiple records in another. This requires a Junction Table (Bridge Table) to function. Example: One Student can enroll in many Courses, and one Course contains many Students. 3. SQL Joins Joins are used to combine rows from two or more tables based on a related column. Join Type Description Inner Join Returns only the records that have matching values in both tables. Left Join Returns all records from the left table and the matched records from the right. Right Join Returns all records from the right table and the matched records from the left. Full Outer Join Returns