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As programmers, we love data. We track our commits, our uptime, and our deployment frequencies. But what about our most important "server"—our heart? 💓 The "Quantified Self" movement has led to an explosion of wearable data. However, if you've ever tried to analyze raw heart rate CSVs (often sampled every few seconds), you'll quickly realize that standard relational databases or even pure Pandas can get sluggish once you hit that 100k+ row mark. In this tutorial, we are going to build a high-performance Quantified Self Dashboard . We will leverage DuckDB —the "SQLite for Analytics"—to perform vectorized execution on heart rate data, paired with Streamlit and Plotly for a slick, interactive frontend. We’ll focus on Python data engineering , time-series analysis , and fast SQL processing . Why DuckDB? 🦆 Traditional databases are row-based, which is great for transactions but terrible for analytical queries. DuckDB is a columnar-vectorized query engine . This means it processes data in chunks (vectors) and utilizes modern CPU instructions (SIMD) to crunch numbers at speeds that make standard Python loops look like they're standing still. The Architecture Here is how our data pipeline flows from raw pixels (well, raw CSV rows) to actionable insights: graph TD A[Raw Heart Rate CSVs] -->|Direct Ingestion| B(DuckDB Engine) B -->|Vectorized SQL Execution| C{Data Aggregation} C -->|Moving Averages/Outliers| D[Streamlit App State] D -->|Plotly| E[Interactive Visualization] E -->|User Input| D Prerequisites 🛠️ Ensure you have the following stack installed: Python 3.9+ DuckDB : For the heavy lifting. Streamlit : For the UI. Plotly : For the beautiful charts. pip install duckdb streamlit plotly pandas Step 1: Ingesting 100,000+ Data Points in Milliseconds One of the coolest features of DuckDB is its ability to query CSV files directly without a formal "import" step. This is a game-changer for developer productivity. import duckdb import pandas as pd # Let's assume 'heart_rate.cs

Although the page itself is more just fun to have made and look at (I like the flip sound), the fun part is how I made it to verify the (and I hate to say it) vibe host service I've been working on. The recent flip board back and forth's on Twitter (X) are what inspired me. The idea here is that people (like me or you) can create something neat like this, and others can remix it, change it and publish their own version. This is that all in action and it worked great. I wrote a blog about it (the

TL;DR — A prototype that works for you but breaks for everyone else usually isn't bad luck. It's four repeatable culprits: you designed for one assembly, your fasteners drift, the enclosure ignores real loads, and you never wrote down why it works. Fix those, and "works on my bench" becomes "works, period." You built the thing, and it works. In your hands, on your bench, every single time. Then a friend tries it, or it sits in the garage a week, or the temperature drops one night, and it just stops. Frustrating doesn't really cover it. Here's the reassuring part: that gap between "works for me" and "works for anyone" is almost always the same small handful of culprits. You're not missing some secret skill. Once you've met them a few times, you start designing around them without even thinking about it. 1. You built it for one. Now build it for two. That first one fit because you were there — nudging, sanding, coaxing it together. The trouble is, all of that lived in your hands, not in the model. So the second copy fights you. If you can't make a second one without the fiddling, it isn't done yet. Bake the clearance into the CAD, then print one you promise not to touch up. That's the real test. 2. Your fasteners are quietly betraying you. Press-fits creep. Hot glue lets go. Jumper wires back out. Double-sided tape taps out the first warm afternoon. I know the boring fixes aren't the fun part — a screw boss, a captive nut, a bit of strain relief, a connector that actually clicks home. But boring is exactly what's still holding a year from now. 3. The enclosure is a load, not a lid. It's easy to treat the box as an afterthought. But heat, dust, and vibration are real forces working on your build. A board that runs cool in the open can slowly cook once it's sealed up. A connector that's happy on the bench can buzz itself loose in a drawer that gets opened every day. Give the heat somewhere to go, mount the board instead of letting it dangle from its wires, and clamp dow

I Built Commercial Redis CDC Source Drivers for Ruby — Here's What I Learned For the past couple of years I've been building a Change Data Capture (CDC) ecosystem for Ruby. Like many CDC projects, it started with PostgreSQL. PostgreSQL's Write-Ahead Log (WAL) is an excellent source of truth: durable, ordered, replayable, and well understood. It provides exactly the properties you want when you're building reliable event pipelines. But the deeper I went into distributed systems, the more I realized something important. Many systems don't observe change from PostgreSQL first. They observe it from Redis. Redis often sits at the front of modern architectures: Redis Streams carry application events. Pub/Sub distributes transient state changes. Keyspace notifications react to cache invalidation and key expiry. Redis Cluster routes events across multiple primaries. In many systems, Redis sees a change before PostgreSQL ever commits it. That raised an interesting question: Can Redis become a first-class Change Data Capture source? The obvious answer is "yes." The interesting answer is "yes—but not in the same way PostgreSQL does." That distinction eventually became cdc-redis-pro , a commercial Redis source driver for the Ruby CDC ecosystem. This article isn't a product announcement. It's an engineering write-up about the architectural decisions behind the project, the tradeoffs Redis forces you to make, and the execution model that ultimately emerged. Redis Doesn't Have One CDC Interface One misconception I frequently encounter is the assumption that Redis has an equivalent of PostgreSQL's WAL. It doesn't. Instead, Redis exposes several completely different mechanisms for observing change. Source Delivery Replay Streams At-least-once Yes Pub/Sub At-most-once No Sharded Pub/Sub At-most-once No Keyspace Notifications At-most-once No At first glance they all look like "events." Operationally they're completely different systems. Streams are durable. Pub/Sub isn't. Keyspace not

After weeks of negotiations, the White House permitted Anthropic to restore access to its most advanced AI model for a select group of US companies and government agencies.

After several years carrying a beta tag, the Kubernetes Security Profiles Operator went 1.0.0 on June 26, freezing eight CRD APIs and clearing a third-party security audit with no criticals. For cluster admins, the practical effect is small but consequential: the syscall and LSM profile a workload runs under is now declared on APIs that will not move under your feet. The release was announced by Sascha Grunert of Red Hat on the CNCF blog. SPO is the Kubernetes operator that manages seccomp, SELinux and AppArmor profiles as cluster-scoped objects, then attaches them to pods. Until now the value proposition was good and the API was provisional. v1.0.0 nails the second half down. What's actually stable All eight CRDs graduated to v1, including SeccompProfile , ProfileRecording , SelinuxProfile , RawSelinuxProfile , and the AppArmor profile type. Conversion webhooks ship with the release, so a cluster running earlier API versions can roll forward without scheduling downtime. The older versions remain available and are slated for removal in a future release. The migration is on the clock, not on fire. The audit pass came with some shape changes that are worth reading before you upgrade. SelinuxProfile swapped its boolean permissive field for a mode enum with Enforcing and Permissive values, which means any GitOps templates that hard-coded permissive: true need a rewrite. RawSelinuxProfile is now gated by an enableRawSelinuxProfiles configuration flag and a validating admission webhook, so the most privileged path through the operator is off by default. AppArmor inputs run through strict regex validation, raw policy payloads are capped at 500 KB, and the eBPF profile recorder picked up explicit resource limits. Why a cluster team should care The point of an operator like this is to take the profile out of the host's filesystem and into the API. That changes the blast radius of "we shipped a container with no profile at all." With SPO and a workload-attached profile, the r

Most poker solvers answer one question very well: given a single hand and a single decision tree, what is the equilibrium strategy? (Yes, there is subgame solving, node locking, and plenty more — but the default frame is still one hand, one equilibrium.) I kept getting stuck on a different one. What if the same kind of spot shows up over and over, and a player can commit to a fixed strategy across those repetitions? In a few toy games I had a hunch, worked out by hand, that committing to a fixed strategy could change its value relative to the one-shot picture. I wanted a tool that could make that commitment value precise — to actually analyze it rather than just believe it. (Whether any of this rises to a repeated-game equilibrium is a much stronger claim, and one I am deliberately not making here.) I'm still learning software engineering, so until recently I couldn't implement this — I was stuck reasoning about toy games on paper. AI tooling made the analysis feasible, so I finally started building it: repeated-poker-analysis . It's a small research project: write one narrow model down, run small examples, and record what the model does and doesn't justify. What repeated-poker-analysis is It is an experimental Python toolkit for small abstract poker games. The current MVP covers: fixed Hero commitment candidates, exact Villain best-response diagnostics in small finite trees, candidate generation and filtering, T_deadline , an economic adaptation deadline, local T_detect , an observable-distribution sensitivity estimate, analysis reports and Markdown summaries. It is small on purpose. It is not a full solver and it is not wired to real solver ranges. It starts from one toy game — a river spot — that is tiny enough to inspect and test by hand. That toy spot is one where showdown always chops but rake still bites. In a single-hand view, putting more money into a raked pot can be locally unattractive. Across repeated occurrences the same spot raises a commitment questi

There is a popular misconception that if you do TDD, your design also stays correct. That if the tests pass, quality is guaranteed. In AI-assisted development, this misconception is the kind that quietly accumulates — the more tests you have, the more invisible damage builds up underneath. All tests passed. The design was still broken. Here is what happened today. A function called safe_post.py had its signature changed. Two arguments — notify_sh and doctor_sh — were removed. The test suite passed in full. But the callers were still using the old signature. They were silently broken. Why did the tests pass? Because the test code itself was using the old signature. The tests had been written (by AI) at a time when the design intent was already misunderstood. The misunderstanding was baked into the tests from the start. Tests passing and the design being correct are two different things. "All tests pass" tells you only one thing: the implementation matches what the tests expect. Whether the tests express the right design intent is a separate question. TDD verifies "implementation against tests" — nothing more Let me restate the TDD definition. Red → Green → Refactor. Write a test. Write the implementation that passes the test. Refactor. In this loop, what the test verifies is whether the implementation meets the test's expectation. That is one verification — and only one. What TDD does not verify is whether the test itself correctly expresses the design intent. The structure looks like this: Design intent → Tests (← this link is not verified) ↓ Implementation (← this link is verified by tests) If the person writing the tests misunderstands the design intent, the tests will pass and the design will still be wrong. Machine learning engineer Hamel Husain calls this the "Gulf of Specification" — the gap between what you intended to measure and what your metric actually measures. Optimize hard against a flawed metric and you optimize hard in the wrong direction. The same d

TL;DR I built a personal VPN using AWS Lambda MicroVMs. Your traffic exits from AWS. When you disconnect, the MicroVM terminates — zero cost, nothing running. When you reconnect, a fresh MicroVM launches in about 20 seconds. ./vpn.sh start # All Mac traffic now exits from AWS ./vpn.sh stop # Back to your real IP Here is what I learned across 12 image builds — dead ends, kernel limitations, and what finally worked. The Idea Lambda MicroVMs launched in June 2026 (4 days ago). They are Firecracker VMs with: Full Linux OS — your own binaries, eBPF, iptables, network namespaces Suspend/resume — state preserved on snapshot, resumes in ~1s per GB (or terminate for zero ongoing cost) Hardware-level isolation — every session gets its own sandbox Per-second billing — ~$0.13/hr for a 2GB ARM64 (Graviton) instance 8-hour max lifetime (active + suspended combined) I wanted to run a VPN inside one. Connect when I need privacy. Disconnect and pay nothing for compute. Resume instantly when I reconnect. Took 12 image builds to get there. What I Tried (and Failed) Attempt 1: NAT Gateway Replacement (The Original Idea) This is actually where the project started. I was paying $32/mo for a NAT Gateway and thought: what if a MicroVM running nftables could replace it? Serverless NAT. Pay only when traffic flows. Why it failed: Lambda MicroVMs cannot act as VPC route targets. Their networking is ingress-only (HTTPS + JWT). Other VPC resources cannot route through a MicroVM. The VPC egress connector gives the MicroVM its own internet access. It does not make it a transit device. That killed the NAT idea. But it made me think — if I cannot route VPC traffic through it, what about routing my own laptop's traffic through it? That is how Serverless VPN was born. Attempt 2: VPC Egress Connector I created a VPC, subnets, security groups, and a network connector. One hour wasted. MicroVMs have INTERNET_EGRESS by default. The connector is only needed for reaching private VPC resources (RDS, interna

AI agents pass tests while producing sloppy thinking. They say "should work" without evidence. They present partial work as complete. They embellish. I built a tiny tool that catches this. It checks any text across four dimensions: Completeness, Consistency, Groundedness, and Honesty. Install pip install self-audit Usage echo "Should work fine. Ready to ship." | self-audit --verbose Completeness: FIXED Groundedness: FIXED [should work fine] FAIL Zero dependencies. Python 3.8+. Stdlib only. 60 lines of core logic. The Four Dimensions Dimension Question What it catches Completeness Did I answer everything? Missing requirements Consistency Did I contradict myself? A-and-not-A patterns Groundedness Did I show evidence? "should work" claims Honesty Am I honest about limits? Embellishment, TODO stubs The dimensions are grounded in Anthropic Constitutional AI framework — Completeness (helpfulness), Groundedness (harmlessness), Honesty (truthfulness), Consistency (rule alignment). Try it on your own output After any AI-assisted coding session, pipe the agent text through self-audit before shipping. You will be surprised what it catches. GitHub: https://github.com/YuhaoLin2005/self-audit Claude Code skill: https://github.com/anthropics/skills/pull/1361

The Post-Mortem Nobody Learns From I've sat through hundreds of post-mortems. Most follow the same pattern: something breaks, someone writes a Google Doc, we have a meeting, we list action items, nobody follows up, the same thing happens again in 3 months. Here's how to break the cycle. The Blameless Culture Trap "Blameless" doesn't mean "actionless." The biggest failure mode I see is teams that use blameless culture as an excuse to avoid accountability. Blameless means: we don't punish the person who pushed the bad deploy. Blameless does NOT mean: nobody is responsible for fixing the systemic issue. My Post-Mortem Template # Incident: [SERVICE] [SYMPTOM] on [DATE] ## Impact - Duration: X minutes - Users affected: N - Revenue impact: $X - SLO budget consumed: X% ## Timeline (UTC) - HH:MM - First alert fired - HH:MM - On-call acknowledged - HH:MM - Root cause identified - HH:MM - Fix deployed - HH:MM - Service recovered - HH:MM - All-clear declared ## Root Cause [2-3 sentences. Technical but readable.] ## Contributing Factors 1. [Factor that made the incident possible] 2. [Factor that made detection slow] 3. [Factor that made resolution slow] ## What Went Well - [Something that worked] - [Something that helped] ## What Went Wrong - [Process failure] - [Technical gap] ## Action Items | Action | Owner | Priority | Due Date | Status | |--------|-------|----------|----------|--------| | ... | ... | P1/P2/P3 | ... | Open | ## Lessons Learned [1-2 paragraphs of genuine insight] The Action Item Problem Action items from post-mortems have a 30% completion rate industry-wide. That's terrible. Here's why: Too many items (I've seen post-mortems with 15 action items) No clear ownership No deadline No follow-up mechanism Competing with feature work The Fix: Three Rules Rule 1: Maximum 3 action items per post-mortem. If you can't narrow it to 3, you haven't identified the real problems. Rule 2: Every action item gets a JIRA ticket linked to the next sprint. Not "someday." Not "bac

the insight that started this project hit me while i was finishing a bytecode-compiled language i'd written in C i'd spent months building a hand-written lexer, a single-pass Pratt compiler, a stack VM with 35 opcodes, and a mark-and-sweep garbage collector. and right near the end i had this realization: an LLM inference engine is the same problem. it's a graph-compile plus memory-plan plus kernel-schedule problem. i'd just built one so i decided to find out if that was actually true the project the result is ignis, a from-scratch LLM inference engine in Rust. i used it specifically to see how far the compiler analogy held up. the dependency count ended up at 2: memmap2 (to mmap the weight blob off disk) and fancy-regex (for one look-ahead in the BPE tokenizer). everything else is hand-written, because the whole point was to understand what's actually happening the compiler analogy holds up better than i expected the interesting part of any inference engine isn't loading the weights or doing matrix math. it's what happens between "here's a compute graph" and "here's an efficient execution plan." that's a compiler problem ignis builds an SSA (static single assignment) IR of the entire Qwen2 forward pass. every operation in the transformer (the RMSNorm layers, the SwiGLU activations, the attention projections, all of it) becomes a node in the graph with explicit data dependencies then fusion passes run over the graph. the intuition is simple: if operation B always and only reads the output of operation A, you can merge them into one op and eliminate the intermediate buffer. in practice this fused 49 RMSNorm ops and 24 SwiGLU ops, bringing the total from 435 operations down to 362 that part felt expected. the liveness analysis surprised me the liveness analysis after fusion, the graph still needs activation buffers: scratch memory to hold intermediate results as the plan executes. the naive approach allocates one buffer per node. the smarter approach asks: which buffer

With discourse about Christopher Nolan’s epic already raging, first viewer reactions will come from mainstream press, not content creators. Some critics are gloating over this break from the norm.

https://archive.md/ArXuF https://www.nbcnews.com/tech/tech-news/us-government-gives-a...

If you ever find yourself with time to kill and crave a fun challenge, you can write a program that prints out its own source code, called a quine ). Go on, give it a try, it's good fun! Once that's done, what's to stop you from modifying the source code instead of printing it verbatim, slowly shifting forms as you iterate on each successive output? Naturally, you'll want to make a game that's played in its own source code (click for an animation): #include<stdio.h> #define z else #define y return #define x int #define w if( #define v putchar( #define B v 10); #define A v 92); /* IOCCC29, w = up, e = down */ x a= 32 ; x b= 6 ; x c= -1 ; x d= 1 ; x e= 5 ; x f= 10 ; x g= 62 ; x h= 5 ; x i[6]={ 1,3,1,4,1,0} ; char*j[]={ "\ \ #include<stdio.h>'#define$z$else'#define$y$return'#define$x$int'#defin\ e$w$if('#define$v$putchar('#define$B$v$10);'#define$A$v$92);''/*$IOCCC\ 29,$w$=$up,$e$=$down$*/''x$a=","32",";x$b=","6",";x$c=","-1",";x$d=","\ 1",";x$e=","5",";x$f=","10",";x$g=","62",";x$h=","5",";x$i[6]={1,3,1,4\ ,1,0};char*j[]={","","};x$k=0;x$l=1;x$m(){l++;w$l==1)y!v$44);w$l==2)y!\ v$34) ;char$o=j[k][l-3];w!o){l=0;k++;y!v$34);}w$o==34){A$y$v $34);\ }w$o= =92){A$y$A}w$o!=32&&o!=1 0)y!v$o);y$m();}void$n(x$o, x$p){\ aspri ntf(j+o,\"%i\",p);}x$mai n(x$o,char**p){char*q;w$c<2 )a+=c\ ;b+=d ;x$r=b+2>f/2&&b<f/2+5;x$s=a+2==g&&b+2>h&&b<h+5;w$c<2){ w$a==\ e+2&& r||s){a-=c;b-=d;c=-c;}w$a<0||a>67){w$a<0){c=2;d=0;}a=3 4;b=6\ ;}w$b<0||b>13){b-=d;d=-d;}w$f/2>10)f-=2;w$h>10)h--;w$o>1){w*p[1]==119&\ &h>0)h--;w*p[1]==101&&h<10)h++;}s=f/2-b+1;w$s<0)f++;w$s>0)f--;}z{b++;w\ $d<0)d++;w$b>=13){w$o>1&&*p[1]==119)d=-4;b=13;}w$f/2<15-i[c-2])f+=2;z$\ e--;w$h<15-i[c-1])h++;z$g--;w$e+3<=0){c++;w$c<7){e=g;f=h*2;g=70;h=15-i\ [c-1];}z{e=5;g=62;c=1;d=1;}}w$a+2==e&&r||s){c=2;e=5;f=28;g=62;h=12;}}n\ \ (1,a);n(3,b);n(5,c);n(7,d);n(9,e);n(11,f);n(13,g);n(15,h);for(s=0;s<","29",";s++){w$s)v$32);q=j[s];r=1;for(char*t=q;*t;t++)w*t==","36",")v$32);z$w*t==","39",")B$z$w*t!=32&&*t!=10){r=0;v*t);w*t==123||*t==125||*t

This is sorely needed, it is helpful for Hawaii as well as for California (cabins on forest service land)

Half-million strong military will train on drones as “universal combat tool.”

We’ve covered so many deals during Prime Day that my head is spinning. But after four days of doing our damndest to try and help folks save money, the thing I’m most hyped for is a simple tool for fixing my most egregious mistakes. I bought a pair of Vampliers. If you’re unfamiliar, Vampliers are […]