Onah Chidera

Mtulivu. Data Scientist.

[Onah Chidera @_iam_bass]

I am not responsible for what i tweet, how you understand it tho is entirely up to you.🙂

[Akintola Steve @Akintola_steve]

You claim you’ve built something really brilliant.

Cool.

I checked it out and, to be fair, it was actually impressive.

Then I said,

“Give me access to the database. I want to try something.”

He did.

I went straight to the audit_logs table and ran a simple UPDATE statement to modify a few records.

Guys…

Guess what happened? !!

[Willie The SRE Guy @willie_sre]

One habit that's saved me countless hours during production incidents:
Don't troubleshoot where the error appears.
Troubleshoot where the request stops.

Logs, traces, and metrics tell different parts of the story.
When you follow the request end-to-end, the real bottleneck usually becomes obvious.

[Suraj Sharma @suraj_sharma14]

If I had 6 months to become an Agentic AI Engineer.

I'd do this.

Stage 1: Python + Async Foundations
asyncio, FastAPI, event-driven architecture, error handling, API integration patterns.

Stage 2: LLM Fundamentals for Agents
Context management, model routing, token economics, latency tradeoffs, failure modes.

Stage 3: Tool Calling + Structured Outputs
Pydantic validation, function calling schemas, error recovery, dynamic tool discovery.

Stage 4: Memory + State Management
Short-term buffers, long-term vector recall, context compression, cross-session sync.

Stage 5: Single Agent Workflows
ReAct loops, plan-and-execute, self-reflection, iteration limits, graceful degradation.

Stage 6: Multi-Agent Orchestration
LangGraph/CrewAI, supervisor patterns, message passing, conflict resolution, handoffs.

Stage 7: Human-in-the-Loop Systems
Uncertainty detection, approval gates, audit trails, resume logic, intervention points.

Stage 8: Evaluation + Quality Assurance
Automated eval harnesses, LLM-as-a-judge, regression testing, hallucination metrics.

Stage 9: Observability + Tracing
Distributed tracing (LangSmith/Arize), cost dashboards, latency monitoring, alerting.

Stage 10: Security + Guardrails
Prompt injection defense, output filtering, PII redaction, sandboxed execution, compliance.

Stage 11: Production Deployment
vLLM/SGLang, Kubernetes scaling, CI/CD for agents, canary releases, rollback strategies.

Stage 12: Open Source + Portfolio
Ship autonomous agents publicly, write architecture docs, record demos, contribute to libs.

Most people stay stuck watching tutorials.

Builders get hired.

(Bookmark it)

[Massimo @Rainmaker1973]

Fast Fourier Analysis in action.

Any complex waveform, sound, or shape can be perfectly reconstructed as the sum of simple rotating circles (epicycles).

[NOVA @Its_Nova1012]

90% of backend developers think these mean the same thing:

- RPC
- REST

They don't.

Do you know the difference?

[Amazing Physics @amazing_physics]

Focault's pendulum which makes the rotation of Earth visible

[Kobolytics @kobolytics]

People are not numbers.

Budgets are not templates.

Your money plan should fit your actual life.

[Tanuj @tanujDE3180]

Backend Interview Question:

Which endpoint scales better ?

Autoincrement vs UUID

[ClaudeDevs @ClaudeDevs]

6 months of Claude Max 20x, on us.

We're expanding Claude for Open Source to more of the community.

If you're a maintainer, a core contributor, someone landing PRs across the ecosystem, or someone keeping a critical package alive, apply today!

[Abhishek Singh @0xlelouch_]

As an AI Engineer.

Please learn:

- LLM fundamentals: tokens, context windows, embeddings, attention, temperature and sampling
- Prompt engineering, structured outputs, function calling and tool use
- RAG properly: chunking, embeddings, reranking, retrieval quality and evaluation
- Vector DBs, hybrid search, metadata filters and freshness of data
- Fine-tuning basics: LoRA, SFT, preference tuning and when not to fine-tune
- Agents, workflows, memory, retries, state machines and human approval steps
- Evals for LLM apps: hallucination checks, groundedness, latency, cost and task success rate
- Guardrails, prompt injection, data leakage and permission boundaries
- Model routing, prompt caching, token optimization and cost per successful task
- AI observability: traces for every LLM call, tool call, retrieval step and final answer
- Production APIs, rate limits, fallbacks, async jobs and handling model failures
- Building with users instead of making another generic chatbot wrapper

Most AI engineers can call an LLM API.

Very few can build an AI product that gives reliable answers, uses the right data, stays within budget, handles failures, and actually improves a real business workflow.

This is what production AI teams actually care about.

[Kobolytics @kobolytics]

Community money needs clarity too.

Ajo.
Esusu.
Cooperative dues.
Group savings.
Family projects.
Track what you contribute so your full money picture stays clear.

[Kobolytics @kobolytics]

Before July starts, don’t just make money goals.

Set up a system.

Track accounts.
Review transactions.
Clean categories.
Watch alerts.
Review weekly.
Clarity needs structure.

[Kobolytics @kobolytics]

Social media affects spending more than people admit.

Ads.
Trends.
Influencers.
Flash sales.
Subscriptions.
FOMO purchases.
If it affects your money, track it.

[Kobolytics @kobolytics]

Happy MSME Day 💚

Small businesses need clear records.

Sales.
Expenses.
Suppliers.
Transfers.
Reports.
You cannot improve what you cannot see.

[Kobolytics @kobolytics]

Tomorrow begins the last few days before July.

Review January to June.

Your second half deserves better clarity.

[Abhishek Singh @0xlelouch_]

90% of backend engineering in 2026 comes down to mastering these 10 concepts:

1) Data modeling + indexes: most outages are self-inflicted N+1 queries and missing composite indexes.

2) Consistency + idempotency: retries happen; design endpoints and consumers so duplicates don’t corrupt state.

3) Caching tradeoffs: know TTLs, stampedes, and cache invalidation paths or Redis becomes a liability.

4) Queues + backpressure: set limits, DLQs, and timeouts so one slow dependency doesn’t melt the whole fleet.

5) Deploys + rollbacks: health checks, canaries, and schema migrations that can roll forward and back.

6) Resource management: connection pools, thread pools, memory, and file descriptors are the real capacity plan.

7) Observability: structured logs, metrics, traces, and good cardinality choices so you can answer why in 5 minutes.

8) Debugging in prod: reproduce with minimal data, inspect live state, and know when to sample, not spam logs.

9) Security basics: least privilege, secrets hygiene, authn/authz separation, and safe defaults on every endpoint.

10) Incident habits: write runbooks, track error budgets, and fix the recurring footguns, not the one-off symptom

[Abhishek Singh @0xlelouch_]

Your application does SELECT * FROM orders WHERE status = 'pending' which returns 100K rows, but you only display 20 on the first page. Server runs out of memory.

How will you fix this?

Topic: Query Optimization

[Param @ParamSiddh]

1) I switched to AI Engineering 1 years ago!
It was the best career move I ever made.

If you want to start today, here's a roadmap:

[Kobolytics @kobolytics]

Better habits protect your future.

Financial habits too.

Track clearly.
Review honestly.
Adjust early.

[Ritesh Roushan @devXritesh]

If I had to learn Backend Development again in 2026,

I'd focus on only these 10 topics:

1. API Design (REST/GraphQL)
2. SQL & Database Design
3. Indexing & Query Optimization
4. Caching (Redis)
5. Authentication & Authorization
6. Message Queues (Kafka/RabbitMQ)
7. System Design Fundamentals
8. Concurrency & Transactions
9. Docker & Deployment
10. Monitoring & Observability

Master these 10.

You'll know more backend engineering than most developers chasing every new framework.