Legendary

|Patriotic Nigerian🇳🇬|
|Software Developer|
|Computer Scientist|
|Programmer|

[Legendary @LegendXy]

The frontier just moved. Are you ready to build on it?

#ExecutionEdgeLtd #ExecutionEdgeLtdUK #ClaudeAI #AI #Anthropic #Claude #ArtificialIntelligence #TechNews

[Legendary @LegendXy]

🚀 Anthropic just changed the game.

Yesterday they launched the Mythos-class — a new tier above Opus — with two models: Claude Fable 5 and Claude Mythos 5.

Quick primer on Claude’s tiers: Haiku (fast & light) → Sonnet (balanced) → Opus (frontier) → and now Mythos sits on top

[Legendary @LegendXy]

Deployed through Project Glasswing with the US government — helping cyber defenders secure critical software.

Use cases I’m watching: long-running autonomous coding agents, deep research workflows, enterprise knowledge work, and defensive security at scale.

[Legendary @LegendXy]

The risks:
Its biggest danger zone is cybersecurity — so Anthropic shipped it with conservative safeguards: sensitive queries get routed to Opus 4.8 instead, triggering in under 5% of sessions.

And Mythos 5?
Same underlying model, safeguards lifted in some areas.

[Legendary @LegendXy]

What Fable 5 can do:
State-of-the-art on nearly all tested AI benchmarks — exceptional in software engineering, knowledge work, vision, and scientific research. The longer and more complex the task, the bigger its lead. It even beat Opus 4.8 by over 10% on some benchmarks.

[Nikki Siapno @NikkiSiapno]

35 system design concepts developers should know:

1. Event-driven architecture
↳ https://t.co/QNUuf1JOy7

2. Redis
↳ https://t.co/YmMKgSvyVk

3. gRPC
↳ https://t.co/QwgTXr1N9z

4. Database types
↳ https://t.co/T0tUF1xYPI

5. Circuit breakers
↳ https://t.co/1U81SrBokz

6. ACID vs BASE
↳ https://t.co/a7nOyylUxk

7. Rate limiting
↳ https://t.co/wr0UAh4sJm

PS - if you want a structured path, get my free 142-page System Design Handbook when you join my free weekly newsletter → https://t.co/rKYuAkfCSL

8. Microservices
↳ https://t.co/1CpY04nNxb

9. System design quality attributes
↳ https://t.co/v9WJoUPevt

10. CAP theorem
↳ https://t.co/ONwpduTOD1

11. Idempotency
↳ https://t.co/2sItwlz1oe

12. REST APIs
↳ https://t.co/GSjtaCU2Y8

13. Sync vs Async
↳ https://t.co/ZXCxP7XrAb

14. Network protocols
↳ https://t.co/tx7MlZQIwE

15. Change Data Capture (CDC)
↳ https://t.co/tgwwoTitCA

16. CI/CD pipelines
↳ https://t.co/SM2YvhioIX

17. SSO (single sign-on)
↳ https://t.co/sNCN5cdt6V

18. JWT
↳ https://t.co/Kuv7DAj6B9

19. Health checks vs heartbeats
↳ https://t.co/r5SalP6CCh

20. API gateway vs load balancer vs reverse proxy
↳ https://t.co/Tg3EhT60tU

21. HTTPS
↳ https://t.co/wc3CQOsmPS

22. Load balancing algorithms
↳ https://t.co/VCLCKOZzni

23. Database caching
↳ https://t.co/23QdZATj2o

24. API protocols
↳ https://t.co/2CEu4Wnhsv

25. CDN
↳ https://t.co/MbaSzBnZPQ

26. Database indexing
↳ https://t.co/uNBq3oqmoS

27. Message Queues
↳ https://t.co/7Tz5sevNA8

28. Hashing vs encryption vs tokenization
↳ https://t.co/6eN9I2YAej

29. Service Discovery
↳ https://t.co/rcKkXkWWcX

30. Pub/Sub
↳ https://t.co/HF0Zr5R4SK

31. Connection pooling
↳ https://t.co/39SsEo4kk3

32. Forward proxy vs reverse proxy
↳ https://t.co/0P6NM8kh8u

33. Consistent hashing
↳ https://t.co/8d8o74EsaS

34. SQL vs NoSQL
↳ https://t.co/oDTRpsnQUn

35. Observability
↳ https://t.co/VjfECfyB9d

What else should be on the list?

——

🔖 Save for later.
♻️ Repost to help others learn and grow.

[Kanika @KanikaBK]

Direct link to doc:
https://t.co/edT5LNIVsj

[Anatoli Kopadze @AnatoliKopadze]

https://t.co/AAWIZD1pNL

[Ahmad @TheAhmadOsman]

Step-By-Step LLM Engineering Projects Roadmap

- Build a tokenizer
- Learn embeddings
- Implement RoPE / ALiBi
- Hand-wire attention
- Build MHA
- Build a Transformer block
- Train a mini-former
- Compare objectives
- Build sampling
- Speculative decoding
- KV cache
- MQA / GQA / MLA
- Long context
- FlashAttention
- Hardware budgets
- Toy MoE
- Sparse model trade-offs
- State-space / linear attention
- Diffusion language models
- Data pipelines
- Synthetic data
- Scaling laws
- SFT / DPO / RLHF / GRPO
- Quantization
- Serving stacks
- Eval harnesses
- RAG
- Tool use / agents
- Vision-language adapters
- Interpretability
- Red-team suite
- Full capstone model system

One request:

Choose an Opensource AI lab when you make it

Opensource is where humanity gets to keep the tools

DM me when you've made it ;)

[Paul Iusztin @pauliusztin_]

5 months ago, I was on a panel with @maximelabonne and @Chip Huyen.

One question has been on my mind ever since:

“How should someone approach getting into AI engineering?”

The biggest mistakes in AI engineering is thinking you need to pick between being a specialist or a generalist.

There’s no correct answer.

It's all about the life you want for yourself.

There's two paths:

1/ Go deep

Become world-class at one thing.

Maybe:

Fine-tuning
Inference optimization
RL
GPU systems
Retrieval
Evaluation

Usually this leads toward:

Bigger companies
More niche roles
Higher compensation
Smaller job pools
Strong technical reputation

2/ Go wide

Become highly versatile.

You understand:

Models
Backend systems
Infrastructure
Data pipelines
Monitoring
Product constraints
Deployment
Agents

You may not be the best at one thing...
But you can build entire systems end-to-end.

This path tends to align much more with:

Startups
Founding engineering
Product building
Entrepreneurial environments
Agentic engineering

Interestingly, the industry is already moving in this direction.

Companies like @Anthropic have popularized roles such as “Technical Staff.”

They sit at the intersection of research, engineering, and product.

I’ve mostly gone wide.
I’m deeply interested in AI...

But I also spent years learning:

Software engineering
Infrastructure
Ops
Data engineering
Distributed systems

Because once AI can help implement many individual components, the leverage moves toward understanding systems.

This doesn’t mean specialization stops mattering.

The highest leverage engineers over the next decade will likely be T-shaped:

Deep in one area
Broad enough to reason across the full stack

There is no objectively “better” path.

Only tradeoffs.

You should optimize for the kind of work and life you enjoy.

Both paths can compound extremely well over time.

P.S. This systems-first way of thinking is exactly how we approach production AI systems inside our Agentic AI Engineering course with @towards_AI

If you want to learn how modern AI systems are designed end-to-end, check it out here: https://t.co/QEuz9UaAkl

[Legendary @LegendXy]

https://t.co/LvTK7jvdIu

[Legendary @LegendXy]

@TheOSSObserver @_avichawla One would be latency, while RAGs make available, additional resources at inference time adding a fractional overhead, fine-tuning seeks to improve at train time and not necessarily inference time.

[Swapna Kumar Panda @swapnakpanda]

Python is a MANDATORY SKILL in 2026.

This is how you can MASTER it in 1 year:

[Paul Iusztin @pauliusztin_]

We just open-sourced the full @aiDotEngineer workshop!

You can clone it and run everything yourself...
→ https://t.co/O7UXJG7sE4

You'll build:
• A Deep Research Agent (grounded search + YouTube analysis)
• A LinkedIn writing workflow (generate → review → edit loops)
• An evals layer to measure quality instead of guessing

But this isn’t just a set of scripts.

It’s a full multi-agent system built around:
• MCP servers (tools, resources, prompts)
• Structured outputs with Pydantic
• Tool-use agents deciding their own flow
• End-to-end pipelines you can actually inspect

You can go through it in 3 steps:
1. Watch the full ~2-hour workshop
2. Run the system on real inputs
3. Rebuild everything yourself with 24 guided tickets

But here’s the interesting part...

You don’t rebuild it manually.
You do it using agentic engineering.

We set up:
• A software engineer agent
• Claude Code subagents
• An orchestrator skill

This takes all 24 tickets and:
• Implements them
• Tests them
• Iterates step by step

So you’re learning how agents work and how to build with agents as collaborators.

This is the same system we use and teach in our Agentic AI Engineering course...

Just compressed into ~2 hours.

If you read the code, you’ll understand more than watching 10 demos.

Link: https://t.co/O7UXJG7sE4

[Shraddha Bharuka @BharukaShraddha]

Millions of people use ChatGPT, Claude, and Gemini every day.

But almost nobody understands what actually happens between hitting Enter and seeing words appear on the screen.

So I'm providing the entire pipeline into one clean visual 👇

Here’s the breakdown:

→ 𝗧𝗼𝗸𝗲𝗻𝗶𝘇𝗲𝗿
Your input isn’t processed as words.
It’s split into tokens.

“gravity” → ["grav", "ity"]

Each token → a numeric ID.
That’s why LLMs struggle with letters — they never truly “see” them.

→ 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝗟𝗮𝘆𝗲𝗿
Every token becomes a high-dimensional vector (e.g. 4096 dims).

This is where meaning begins:
Similar words → closer in vector space.

→ 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿 𝗕𝗹𝗼𝗰𝗸𝘀 (×𝗡)
The real intelligence lives here.

• Self-attention (Q, K, V) → tokens “look” at each other
• FFN → processes each position
• Repeated dozens of times

This is how context is built.

→ 𝗞𝗩 𝗖𝗮𝗰𝗵𝗲
The most important optimization.

Instead of recomputing everything →
the model stores past attention (K, V)

⚡ Faster generation
⚠️ But memory grows with sequence length

→ This is the real bottleneck

→ 𝗦𝗮𝗺𝗽𝗹𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆
The model doesn’t pick words.
It outputs probabilities.

How you sample = how it behaves:

• Greedy → predictable
• Top-K → limited randomness
• Top-P → balanced
• Temperature → creativity control

Same model. Completely different outputs.

→ 𝗦𝗽𝗲𝗰𝘂𝗹𝗮𝘁𝗶𝘃𝗲 𝗗𝗲𝗰𝗼𝗱𝗶𝗻𝗴
A hidden speed hack.

• Small model predicts ahead
• Large model verifies in parallel

✅ If correct → multiple tokens generated instantly

This is how responses feel fast.

→ 𝗗𝗲𝘁𝗼𝗸𝗲𝗻𝗶𝘇𝗲𝗿 + 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴
Token IDs → converted back to text

That “typing” effect you see?
It’s not UI animation.

It’s literally token-by-token generation.

⚠️ Two things most people miss:

→ 𝗣𝗿𝗲𝗳𝗶𝗹𝗹 phase = compute-heavy
→ 𝗗𝗲𝗰𝗼𝗱𝗲 phase = memory-heavy

Different problems. Different optimizations.

This is why AI inference is still expensive.

I’ve studied these systems deeply…
and honestly, the more you learn, the crazier it gets.

It’s not just AI.
It’s engineering at insane scale.

Which part of this pipeline surprised you the most? 👇

[Vaidehi @Ai_Vaidehi]

𝟯𝟬 𝗕𝗹𝗼𝗴𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝟯𝟬 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:

1. APIs: https://t.co/7fL4F9XGef
2. API Gateways: https://t.co/D4j6P6CYDr
3. JWTs: https://t.co/OKiOvlj8Fq
4. Webhooks: https://t.co/nvgjOsKOas
5. REST vs GraphQL: https://t.co/Kipxik9t7B
6. Load Balancing: https://t.co/34mFEh55xv
7. Proxy vs Reverse Proxy: https://t.co/IDslD2PJcL
8. Scalability: https://t.co/ohmTKQxPZZ
9. Availability: https://t.co/GkquXXNylU
10. SPOF: https://t.co/GQ8WR0E8RP
11. CAP Theorem: https://t.co/5bu4SkN9lJ
12. SQL vs NoSQL: https://t.co/4aMsfVsZbD
13. ACID Transactions: https://t.co/52MERICrzU
14. Database Indexes: https://t.co/BBoa8EMX2r
15. Database Sharding: https://t.co/rZ2ZxQS11P
16. Consistent Hashing: https://t.co/qKWIFOXKql
17. CDC: https://t.co/kBldaWUZzb
18. Caching: https://t.co/lMkqrsputO
19. Caching Strategies: https://t.co/pXru61Cfvr
20. Cache Eviction Policies: https://t.co/EoeeW1FFRy
21. CDN: https://t.co/0PcOUhdNXp
22. Rate Limiting Algorithms: https://t.co/4ocDZggbho
23. Message Queues: https://t.co/FTPz4XpgqG
24. Bloom Filters: https://t.co/ym7I98Aboj
25. Idempotency: https://t.co/itqXVPe4zT
26. Concurrency vs Parallelism: https://t.co/CbjqqzYKIx
27. Long Polling vs WebSockets: https://t.co/L1Tr4MeN5A
28. Stateful vs. Stateless Architecture: https://t.co/0AVkCh9fm1
29. Batch vs Stream Processing: https://t.co/wMWEf8IGFY
30. Geohashing: https://t.co/emPySzIVP8

🔁 Repost to help a friend.
📌 Save this post for later.

[Shraddha Bharuka @BharukaShraddha]

MASTER SYSTEM DESIGN

SYSTEM DESIGN MASTER TREE
│
├── 1. Fundamentals
│ ├── What is System Design
│ ├── Functional Requirements
│ ├── Non-Functional Requirements
│ │ ├── Scalability
│ │ ├── Reliability
│ │ ├── Availability
│ │ ├── Consistency
│ │ └── Performance
│ └── Trade-offs
│
├── 2. Architecture Basics
│ ├── Monolithic Architecture
│ ├── Microservices Architecture
│ ├── Service-Oriented Architecture (SOA)
│ ├── Layered Architecture
│ └── Event-Driven Architecture
│
├── 3. Networking Basics
│ ├── HTTP / HTTPS
│ ├── TCP / IP
│ ├── DNS
│ ├── Load Balancing Concepts
│ └── Latency vs Throughput
│
├── 4. Load Balancing
│ ├── Types (L4 / L7)
│ ├── Algorithms
│ │ ├── Round Robin
│ │ ├── Least Connections
│ │ └── IP Hash
│ ├── Reverse Proxy
│ └── Failover Strategies
│
├── 5. Databases
│ ├── SQL vs NoSQL
│ ├── Relational Databases
│ ├── NoSQL Types
│ │ ├── Key-Value
│ │ ├── Document
│ │ ├── Column
│ │ └── Graph
│ ├── CAP Theorem
│ └── ACID vs BASE
│
├── 6. Caching
│ ├── Why Caching
│ ├── Cache Layers
│ │ ├── Client-side
│ │ ├── CDN
│ │ ├─�� Server-side
│ ├── Cache Strategies
│ │ ├── Write-through
│ │ ├── Write-back
│ │ └── Cache-aside
│ └── Cache Invalidation
│
├── 7. Data Partitioning
│ ├── Vertical Partitioning
│ ├── Horizontal Partitioning (Sharding)
│ ├── Consistent Hashing
│ └── Rebalancing
│
├── 8. Replication
│ ├── Master-Slave
│ ├── Master-Master
│ ├── Synchronous Replication
│ └── Asynchronous Replication
│
├── 9. Consistency Models
│ ├── Strong Consistency
│ ├── Eventual Consistency
│ ├── Causal Consistency
│ └── Read-after-Write Consistency
│
├── 10. Messaging Systems
│ ├── Message Queues
│ ├── Pub/Sub Systems
│ ├── Kafka / RabbitMQ
│ ├── Event Streaming
│ └── Exactly-once / At-least-once Delivery
│
├── 11. API Design
│ ├── REST
│ ├── GraphQL
│ ├── gRPC
│ ├── Rate Limiting
│ └── API Gateway
│
├── 12. Storage Systems
│ ├── File Storage
│ ├── Object Storage (S3)
│ ├── Block Storage
│ └── CDN (Content Delivery Network)
│
├── 13. Security
│ ├── Authentication
│ ├── Authorization
│ ├── Encryption (SSL/TLS)
│ ├── Firewalls
│ └── DDoS Protection
│
├── 14. Scalability
│ ├── Vertical Scaling
│ ├── Horizontal Scaling
│ ├── Auto Scaling
│ └── Stateless Services
│
├── 15. Fault Tolerance
│ ├── Redundancy
│ ├── Circuit Breaker
│ ├── Retries & Backoff
│ └── Graceful Degradation
│
├── 16. Observability
│ ├── Logging
│ ├── Monitoring
│ ├── Metrics
│ ├── Alerting
│ └── Distributed Tracing
│
├── 17. Deployment & DevOps
│ ├── CI/CD
│ ├── Docker
│ ├── Kubernetes
│ ├── Blue-Green Deployment
│ └── Canary Releases
│
└── 18. Real-World System Design
├── URL Shortener (like Bitly)
├── Chat System (like WhatsApp)
├── Video Streaming (like YouTube)
├── Ride Sharing (like Uber)
└── Social Media Feed (like Twitter/X)

[Nikki Siapno @NikkiSiapno]

SLO vs SLI vs SLA

An 𝗦𝗟𝗜 (Service Level Indicator) measures what’s actually happening in your system; like request latency, error rate, or uptime. It’s the raw signal that tells you how your service is performing.

An 𝗦𝗟𝗢 (Service Level Objective) defines the acceptable range for an SLI; like “99.9% of requests succeed within 200ms.” It’s what your team aims to achieve to maintain reliability.

An 𝗦𝗟𝗔 (Service Level Agreement) is a formal contract with users or customers, often tied to penalties if targets aren’t met. It defines the consequences, not just the goal.

And when failures occur, that’s where postmortems should close the loop.

But here’s the gap:

Most postmortems are written after the fact. Digging through Slack. Rebuilding timelines. Guessing what actually happened. Which means they’re slow, and painful. So they get deprioritised, half-finished, or never written at all.

That’s not just a process problem. It’s an experience and tooling problem.

incident[.]io’s new postmortem workflow flips this:
→ It builds the draft for you from real incident data
→ So you start with context, not a blank page
→ It turns postmortems into a collaborative, structured workflow that’s actually easy to write and complete

Worth a read → https://t.co/bZ3jEokLnf

SLIs tell you what happened.
SLOs define what should happen.
SLAs define what happens if you fail.
Postmortems are where you make sure it doesn’t happen again.

What else would you add?

——

♻️ Repost to help others learn and grow.
🙏 Thanks to @incident_io for sponsoring this post.
➕ Follow me ( Nikki Siapno ) to improve at system design.

[Avi Chawla @_avichawla]

A tricky LLM interview question:

You're fine-tuning a model for Python code generation. The data was generated using the strongest LLMs like Opus/GPT.

But the fine-tuned model performs better when you use a weaker teacher instead.

How could this happen?

(answer below)

[vixhaℓ @TheVixhal]

https://t.co/dPZaRdRfrt