Michael Ikechukwu

God's Child | Computer Programmer | Krumper | Tech Otaku | Weeb | Professional Gamer(Car Racing Enthusiast)

[Rahul @sairahul1]

This is the best site on the internet to learn harness engineering.

Free. Completely.

Most AI engineers have never heard the term.

https://t.co/bwDbTTYsjM

Bookmark this site.

Then read this setup ↓

[nisedo @nisedo_]

A must read

https://t.co/d4tiRoEvpI

[Kilo @kilocode]

https://t.co/KhtThIg0C5

[Dee Macé @frmarcellinus]

Some self-worshipping and self-discovery Christians say "God is not the Church." "The Kingdom of God is within me, so I need not go to church." LET ME TELL YOU HOW THEY GOT IT WRONG IN 6 POINTS.

1. The self-worshipping spirituality of today tries to turn God into a private, customizable therapist. But the Bible doesn't know anything about a "solo Christian." YOU CANNOT BE A SOLO CHRISTIAN.

2. God no longer dwells in physical buildings. The Church, the gathered community of believers, is now God's official temple where His Spirit lives (1 Corinthians 3:16).

3. When Saul persecuted the early believers, Jesus didn't ask, "Why are you hurting my people?" He said, "Why are you persecuting me?" (Acts 9:4). Jesus completely identifies Himself with His Church.

4. The Bible calls the Church the "Bride of Christ" and says the two become "one flesh" (Ephesians 5:31-32). You cannot love the Husband (Jesus) while rejecting His Wife (the Church).

5. Jesus uniquely promises His presence and authority wherever believers gather in His name (Matthew 18:20). He is found in community, not just in isolation.

6. If the Church is Christ's Body, you can't have the Head without the limbs. If the Church is Christ's Bride, you can't love the Husband and despise His wife. If the Church is God's Temple, you can't find His fullness outside of His house. Persecuting, ignoring, or abandoning the Church is, by Jesus' own definition, doing those exact things to Him.

[Kilo @kilocode]

Two open models in Kilo tonight. Both free. What are YOU doing with your evening? 👀

Nemotron 3 Ultra (@nvidia): the strongest US open-weights model out right now. Built for agentic work, 5x faster inference, 1M context. Point it at something gnarly and walk away.

Step 3.7 Flash (@StepFun_ai): multimodal, 400 tok/sec, 256k context. Near-Sonnet coding for basically nothing.

The models are free. Excuses are not. Go build!

[aditya @adxtyahq]

for anyone asking where to learn this stuff:

• RAG → https://t.co/4bzbUIwV5g
• Agentic RAG → https://t.co/IotOiGmV1Y
• AI Agents → https://t.co/nEeMnVJQbk
• Multi-Agent Systems → https://t.co/pavDPVJEFj
• LangGraph → https://t.co/3miEqqFzF0
• LangGraph (code) → https://t.co/v7kxHZXqba
• MCP → https://t.co/lKawRb4etX
• Memory Systems → https://t.co/LSaT2UaPAS
• Evals → https://t.co/vxChxa1kqQ
• Context Engineering → search "Context Engineering Survey" on arXiv

and please skip the "build an ai agent in 10 minutes" videos
build something, watch it fail, then figure out why.

[Felipe Demartini @namcios]

30 anos.

Por 30 anos o PC foi a mesma coisa: Intel ou AMD dentro, GPU do lado, e torce pra não travar.

A NVIDIA acabou com isso numa keynote.

RTX Spark. Primeiro chip deles para computador pessoal. CPU, GPU e memória num único sil��cio. ARM, 3nm, 1 petaflop de IA local.

Num laptop de 14mm.

Rodou Forza Horizon 6 e 007 First Light no palco a 100 FPS em 1440p. Fora da tomada. Sem throttling. No Windows.

O número que muda tudo: roda modelos de IA de 120 bilhões de parâmetros sem cloud. Sem API. Sem assinatura. Seu agente de IA mora na sua máquina. Ligado 24 horas. Só seu.

O PC não é mais uma tela com teclado. É uma estação de IA pessoal.

[DHH @dhh]

You can’t outwork the whole world. There’s always going to be someone somewhere willing to work as hard as you. Someone just as hungry. Or hungrier.

Assuming you can work harder and longer than someone else is giving yourself too much credit for your effort and not enough for theirs. Putting in 1,001 hours to someone else’s 1,000 isn’t going to tip the scale in your favor.

What’s worse is when management holds up certain people as having a great “work ethic” because they’re always around, always available, always working. That’s a terrible example of a work ethic and a great example of someone who’s overworked.

A great work ethic isn’t about working whenever you’re called upon. It’s about doing what you say you’re going to do, putting in a fair day’s work, respecting the work, respecting the customer, respecting coworkers, not wasting time, not creating unnecessary work for other people, and not being a bottleneck. Work ethic is about being a fundamentally good person that others can count on and enjoy working with.

So how do people get ahead if it’s not about outworking everyone else?

People make it because they’re talented, they’re lucky, they’re in the right place at the right time, they know how to work with other people, they know how to sell an idea, they know what moves people, they can tell a story, they know which details matter and which don’t, they can see the big and small pictures in every situation, and they know how to do something with an opportunity. And for so many other reasons.

So get the outwork myth out of your head. Stop equating work ethic with excessive work hours. Neither is going to get you ahead or help you find calm.

[The Outwork Myth — It Doesn't Have To Be Crazy At Work, 2018]

[Pavlo Molchanov @PavloMolchanov]

🚀 Self-speculation brings 6.75x real speedup for LLM generation with SGLang inference!

Same model drafts future tokens in Diffusion mode → then verifies them in AR (causal) mode. One model and one KV cache. Just different attention masks.

Thanks to perfect alignment, we get 2× longer acceptance lengths than MTP techniques (Eagle-3, MTP, dFlash).

We run 2 forward passes… but the 2× higher acceptance means we break even - and with zero overhead from extra drafter, KV cache, or LM head that comes with MTP - those are not free.

Last week we released Nemotron-Labs-Diffusion + Tri-mode LLMs! We did continued pre-training on Ministral-3 models by switching attention patterns (block causal <> bidirectional). Result: one model that runs AR mode, Diffusion mode, and Self-Speculation.

Diffusion mode already shows high benchmark accuracy - excited to see what happens when someone beats left-to-right acceptance! 🔥

Github: https://t.co/Zqbw3KcAyF
Paper: https://t.co/rp86A7D0xJ
SGLang inference: https://t.co/uTgZPALEJl
Try the models on HF: https://t.co/1zStcCCWPi

[MANKEMISTA @mankemista]

@BlueBeastx12 First ninja to use a domain expansion

[Michael Ikechukwu @Badass_Dev]

@bahdcoder 😂😂😂😂😂

[Michael Ikechukwu @Badass_Dev]

@pipe_dev wtf he died omo

[Rohan Paul @rohanpaul_ai]

Ex Google CEO, Dr. Eric Schmidt: AI may hit a money wall before it hits a power wall.

"The real limit to AI is not energy; it is actually cash. When you add up the cost of these things, if you take round numbers, say $50 billion per gigawatt, then 10 gigawatts is half a trillion dollars.

How many companies, countries, and so forth can hand an industry a trillion dollars of capital? Very, very few. The Chinese could certainly do it. I do not know if they are doing it, but I am going to try to find out.

In America, there are people who hope that is going to happen. It is interesting that you can finance these things because the brilliance of the American capital market allows us to borrow that kind of money. For example, the Europeans cannot do this, which they are sort of sore about."

---

Full video from 'Special Competitive Studies Project' YT channel ( link in comment)

[Michael Ikechukwu @Badass_Dev]

@TEEPHTREND 🤣😂🤣😂🤣😂🤣....nawa o

[Michael Ikechukwu @Badass_Dev]

@bigbrutha_ 😂😂

[chinedu🦀 @chinedu_10]

Something most developers don’t think about…

OLTP and OLAP both use SQL and query tables, but they solve completely different problems.

OLTP powers your app: small reads, fast writes, low latency. Users create records, update data, and interact in real time.

OLAP serves your analyst: scanning millions of rows and crunching a few columns. Questions like “What was total revenue across all stores last quarter?” are what OLAP is optimized for.

Running both on the same database kills production performance with one heavy report query.

That’s why data warehouses exist: pull data from production, transform it, and load it into a system built for analytics (ETL).

Same SQL. Completely different beasts underneath.

[Osaretin Victor Asemota @asemota]

People were ALWAYS going to leave. A pipeline focused on learning and knowledge transfer addresses that. My worry is that product companies are complaining so much when most of the learning was supposed to be embedded in the product and the processes around it, making it easier for people to pick up when those before them inevitably leave.

There is a deeper foundational organizational design problem here that has not been adequately addressed. I went through this for a decade and was getting burned out with training people for others until @JosephBFuller cracked this for me at a course at HBS.

There is ZERO equity in services. You MUST productize EVERYTHING to keep knowledge within the organization. These companies' complaints are a symptom of a wider problem: a lack of products within the general ecosystem. There are many things you shouldn't have people keeping in their heads.

There is room to build many things beyond the payment products you are building, and you should invest in them. Google is my go-to on this. Amazon turned its people into products, and they got AWS from it.

[Olaonipekun BSc, MSc, PhD in-view 👐 @OfficialSamkayz]

Another reason they think we are not good enough in Nigeria is that they want us to know everything.

It's only in Nigeria they will expect some with 1yrs experience to know Docker, Kubernetes, Messaging Queues, and advance DSA before they can employ you.

[Michael Ikechukwu @Badass_Dev]

@Eniola_OE @bigbrutha_ damn!!

[Justin Skycak @justinskycak]

A surprising amount of adulthood is finally realizing that structure is not the opposite of freedom. It’s the thing that keeps freedom from turning into aimlessness.