Ea-chap

Yann LeCun was right the entire time. And generative AI might be a dead end. For the last three years, the entire industry has been obsessed with building bigger LLMs. Trillions of parameters. Billions in compute. The theory was simple: if you make the model big enough, it will eventually understand how the world works. Yann LeCun said that was stupid. He argued that generative AI is fundamentally inefficient. When an AI predicts the next word, or generates the next pixel, it wastes massive amounts of compute on surface-level details. It memorizes patterns instead of learning the actual physics of reality. He proposed a different path: JEPA (Joint-Embedding Predictive Architecture). Instead of forcing the AI to paint the world pixel by pixel, JEPA forces it to predict abstract concepts. It predicts what happens next in a compressed "thought space." But for years, JEPA had a fatal flaw. It suffered from "representation collapse." Because the AI was allowed to simplify reality, it would cheat. It would simplify everything so much that a dog, a car, and a human all looked identical. It learned nothing. To fix it, engineers had to use insanely complex hacks, frozen encoders, and massive compute overheads. Until today. Researchers just dropped a paper called "LeWorldModel" (LeWM). They completely solved the collapse problem. They replaced the complex engineering hacks with a single, elegant mathematical regularizer. It forces the AI's internal "thoughts" into a perfect Gaussian distribution. The AI can no longer cheat. It is forced to understand the physical structure of reality to make its predictions. The results completely rewrite the economics of AI. LeWM didn't need a massive, centralized supercomputer. It has just 15 million parameters. It trains on a single, standard GPU in a few hours. Yet it plans 48x faster than massive foundation world models. It intrinsically understands physics. It instantly detects impossible events. We spent billions trying to force massive server farms to memorize the internet. Now, a tiny model running locally on a single graphics card is actually learning how the real world works.

@python_xi this is 🔥, kudos pythonxi

@askghmedia Folks, this actually depends on the amount you're earning.

“I used to spend 20% of my income. Now my wealth is so large that I can’t even spend 2% of it.” — Ghanaian business mogul Richard Nii-Armah Quaye

🚨 BREAKING: Someone just open-sourced software that sees you through walls using only WIFI signals. it’s called WiFi-DensePose. It maps your exact body pose in real-time. no cameras. no sensors. just your living room router. 100% Open Source.

@thecreativexx Nice storytelling — the narrative style really shines through here. It has that distinct gawx art vibe all over it.

What did I just watch???? 🤯🤯🤯

Software engineers are going to love this! I found an open-source error monitoring agent that scans production logs, finds the root cause, and sends a Slack message with full context before you even notice something broke. Cuts down production downtime by 95%! Check this:

🚨 Someone just open sourced a fully autonomous AI hacker and it's terrifying. It's called Shannon. Point it at your web app, and it doesn't just scan for vulnerabilities. It actually exploits them. Real injections. Real auth bypasses. Real database exfiltrations. Not alerts. Not warnings. Actual working exploits with copy-paste proof-of-concepts. Here's what this thing does autonomously: → Reads your entire source code to plan its attack → Maps every endpoint, API route, and auth mechanism → Runs Nmap, Subfinder, and WhatWeb for deep recon → Hunts for Injection, XSS, SSRF, and broken auth in parallel → Launches real browser-based exploits to prove each vulnerability → Generates a pentester-grade report with reproducible PoCs Here's the wildest part: It follows a strict "No Exploit, No Report" policy. If it can't actually break it, it doesn't report it. Zero false positives. It pointed at OWASP Juice Shop and found 20+ critical vulnerabilities in a single run including complete auth bypass and full database exfiltration. On the XBOW Benchmark (hint-free, source-aware), it scored 96.15%. Your team ships code daily with Claude Code and Cursor. Your pentest happens once a year. That's 364 days of shipping blind. Shannon closes that gap. One command. Fully autonomous. The Red Team to your vibe-coding Blue team. Every Claude coder deserves their Shannon. 10.6K GitHub stars. 1.3K forks. Already trending. 100% Open Source. AGPL-3.0 License.

If you want to become a better software engineer (in 2026), read these 15 engineering blogs: 1. Uber Engineering ↳ uber.com/blog/engineeri… 2. Airbnb Tech Blog ↳ airbnb.tech/blog/ 3. Meta Engineering ↳ engineering.fb.com 4. AWS Architecture ↳ aws.amazon.com/blogs/architec… 5. Netflix TechBlog ↳ netflixtechblog.com 6. Discord Engineering ↳ discord.com/category/engin… 7. Google Research ↳ research.google/blog 8. NVIDIA Developer ↳ developer.nvidia.com/blog 9. Slack Engineering ↳ slack.engineering 10. Cloudflare Blog ↳ blog.cloudflare.com/tag/engineering 11. Figma Tech Blog ↳ figma.com/blog/engineeri… 12. Shopify Engineering ↳ shopify.engineering 13. Stripe Engineering ↳ stripe.com/blog/engineeri… 14. Microsoft Engineering ↳ devblogs.microsoft.com/engineering-at… 15. GitHub Engineering ↳ github.blog/engineering Bonus (system design deep dives): Level Up Coding ↳ lucode.co/luc-system-des… What other blogs should be on this list? -- 👋 PS: Get our System Design Handbook FREE when you join our newsletter. Join 28,001+ engineers: lucode.co/luc-newsletter… -- 🔖 Save for later. ♻️ Repost to help other engineers learn and grow. ➕ Follow Nikki Siapno + turn on notifications.

As a Backend Engineer in 2026 aiming for Staff, please learn: 1. One language deeply (Go/Rust/Java) Not “I can write APIs”, but runtime model, memory, concurrency, profiling, GC behavior (if any), and how to read stack traces like a native. 2. Data modeling and storage fundamentals Relational modeling, constraints, isolation levels, indexes, query plans, locks, deadlocks, migrations, backup/restore, partitioning. Most “scaling” problems are schema + query shape problems. 3. Distributed systems basics that actually show up in prod Consistency vs availability, timeouts, retries, idempotency, backpressure, message ordering, leader election, clock skew, eventual consistency, and what happens during partial failures. 4. API design and contracts Versioning, pagination, filtering, error models, idempotency keys, rate limits, backwards compatibility, and how to avoid breaking mobile clients for months. 5. Performance and capacity engineering Latency budgets (p50/p95/p99), tail latency causes, load testing, queueing theory intuition, connection pools, CPU vs IO bound, and capacity planning with real numbers. 6. Reliability engineering SLOs/SLIs, incident response, postmortems, alerting that does not spam, error budgets, graceful degradation, feature flags, circuit breakers, bulkheads. 7. Observability like a pro Structured logs, metrics, tracing, correlation ids, RED/USE metrics, sampling strategies, and how to debug “it is slow sometimes” without just guessing. 8. Security fundamentals AuthN/AuthZ, least privilege, secrets management, token expiry, OWASP basics, SSRF, injection, secure defaults, audit logs, threat modeling for your own services. 9. Messaging and async systems Kafka/Rabbit/SQS semantics, at-least-once vs exactly-once (and why “exactly once” is mostly a marketing term), consumer groups, retries, DLQs, replay, dedupe. 10. Caching with correctness Cache invalidation strategies, TTLs, stampede protection, read-through/write-through, negative caching, and when caching makes bugs harder than latency. 11. Infrastructure literacy Linux basics, networking (DNS, TCP, TLS), containers, k8s concepts, autoscaling, deployment strategies (blue/green, canary), and what your cloud bill is really paying for. 12. System design, but with tradeoffs Designing is picking pain. Learn to write down constraints, failure modes, data growth, and operational cost. Staff is judged on tradeoffs, not diagrams. 13. Codebase leadership Design docs, RFCs, review quality, mentorship, aligning teams, reducing complexity, owning a subsystem end-to-end, making boring systems that do not wake people at 2am. 14. Pick ONE domain to go deep Payments, search, streaming, identity, infra, data platform, etc. Staff engineers are “the person for a hard area”, not generic API writers. Stop hopping stacks every month. Pick a lane, build proof of reliability, and become the person people call when prod is on fire. That is Staff.

You're probably sick of me saying "B-tree" but these impact SO MUCH of database performance. They're used all over the place in Postgres, MySQL, and SQLite. This week I broke down B-tree lookups and how the page cache makes lookups faster.

How do GPUs work?

@aky3k31212 dijkstra's algorithm

Ghanaians ankasa we get problem ooo

Huge computer science result: A Tsinghua professor JUST discovered the fastest shortest path algorithm for graphs in 40yrs. This improves on Turing award winner Tarjan’s O(m + nlogn) with Dijkstra’s, something every Computer Science student learns in college.

@newbie_567 😂

😭😭😭😭😭😭😭

MQTT - Postgres + PostGis - Kafka - Device sends its ID and geopos with MQTT (doesn't drain battery as HTTP Requests) - Kafka receives MQTT event - Using Postgres you store a state machine for every driver, and with PostGis you apply your geopositional logic

"I use AI in a separate window. I don't enjoy Cursor or Windsurf, I can literally feel competence draining out of my fingers." @dhh, the legendary programmer and creator of Ruby on Rails has the most beautiful and philosophical idea about what AI takes away from programmers.

No disrespect to Linus Torvalds, but this guy is the greatest geek alive 🫡 Created UNIX in 1971 when he was 28 years old. Created Go in 2009 when he was 66 years old😲 He also developed the B programming language (which led to C), created UTF-8 encoding (making international text possible online), and designed essential tools like grep that developers still rely on daily. He also helped with the development of Multics (that led to UNIX), Plan 9 from Bell Labs and Inferno operating systems. That's 4 operating systems in total... Most people don't even use these many OS. Pretty impressive resume, right? 🔥 And it's a shame that many people, even the ones in the IT and tech industry, don't know him. Ken Thompson.... Remember the name 🙏

@the_Lawrenz I know a bait when I see one

Corruption aside, Nigeria is more developed, advanced and richer than Saudi Arabia.

@CinephileCentre He had every reason to become a villain, yet chose to be a superhero.

Tobey’s Spider-Man really said with great power comes great bills, great heartbreak, and great depression.

@totocycline I recommend @P4G_WEARGH for locally made shoes! chale, What you see is what you get—great quality and craftsmanship. They're really good.

See what I ordered vs what ordered me lmaooo