Mutuku Makau

Data driven healthcare. AI. Patient Safety. Healthcare Finance. #rstats

[Mutuku Makau @drmakau]

@maattttbrown @SFGate FAKE NEWS. Here's the real video https://t.co/4OdqbatORs.

[Mike McQuaid @MikeMcQuaid]

Today, I’m proud to announce Homebrew 6.0.0.

Since 5.1.0: secure tap trusting, faster JSON API, Linux sandboxing, better defaults, brew bundle improvements, improved performance, initial macOS Golden Gate support.

https://t.co/xUd9VLksEW

[Mutuku Makau @drmakau]

@omuonyotieno @AmbokoJH I think you miss the point on the wider impact of taxation burden on the common man.

[Mutuku Makau @drmakau]

@greyc111 @dkmaraga @citizentvkenya @NationAfrica @TheStarKenya @KBCChannel1 @KTNNewsKE @StandardKenya @K24Tv @UGMParty @Maraga27 @NTVnewsroom You wanna wait for confirmation?
STAND UP for your fellow citizens.

[Carlos E. Perez @IntuitMachine]

🧵 PEEK: The 1k-Token Map That Just Killed the Long-Context Tax

Your LLM agent is reading the same 50k-token codebase for the 20th time.

It still doesn't know where anything is.

PEEK from @Microsoft just changed that with a 1k-token "context map" that:

• ↑ 34% accuracy
• ↓ 93–145 fewer retries
• 5.8× cheaper than prompt tuning

Here's how: 🧵

Every time you ask GPT-5 a new question about the same repo, it re-discovers:

→ File structure
→ Key classes
→ How modules connect

You're paying for the same orientation work. Again. And again.

Industry calls this "the long-context tax."

PEEK's breakthrough:

Separate "context understanding" from "task execution."

Instead of stuffing everything into the prompt or retrieving blindly, agents now maintain a tiny persistent map — like a cheat-sheet they write once and reuse forever.

The Context Map has 5 sections:
1⃣ Context Roadmap — high-level structure
2⃣ Context Understanding — key entities/relationships
3⃣ Domain Constants (if needed)
4⃣ Parsing Schemas
5⃣ Reusable Results (cached answers)

Budget: exactly 1,024 tokens.

Three modules keep it fresh without bloat:

🔍 Distiller → Extracts only transferable orientation knowledge
✏️ Cartographer → Makes clean, deduplicated edits (ADD/DELETE/REPLACE)
🗑️ Evictor → Drops low-priority items when budget fills

Separation matters: mixed roles = noise + duplication.

Tested on OOLONG + CL-bench (coding benchmarks):

MetricGain vs. ACE (SOTA)Accuracy+6–34%Iterations saved93–145 fewerCost reduction1.4–5.8× cheaper

Same base model. Same agent. Just 1k tokens of orientation cache.

Here's the efficiency secret:

Freeze the map after 1–4 queries.

You get 80%+ of the gains but near-zero maintenance cost after that.
Most "learning" systems never stop updating → wasted compute.
PEEK learns fast, then locks in.

How PEEK beats the field:

❌ RAG: retrieves fragments, no holistic structure
❌ Summarization: compresses content, not orientation
❌ ACE/prompt tuning: optimizes tasks, not context understanding
✅ PEEK: caches the mental model your agent should have built on day 1

Devil's advocate:

PEEK wins when context is structured and queries recur.

If you're writing one-off creative fiction or chatting about random PDFs, the map has less to cache.
But for repos, enterprise docs, analytics? This is the new baseline.

Traditional stack:
→ Bigger context windows
→ Better retrieval
→ Smarter prompts
New stack:
→ Bigger context windows
→ Better retrieval
→ Persistent orientation caches

Context understanding just became a first-class versioned artifact.

Two multipliers you can stack today:

1⃣ PEEK-style maps (↓ redundant reasoning)
2⃣ KV-cache optimizations (↓ redundant token processing)
Combine them = multiplicative inference savings.

The next wave of agent infra will bake both in by default.

If you're building agents that interact with the same long contexts repeatedly:

→ Stop re-engineering prompts every query
→ Start caching orientation knowledge

The 1k-token map is the missing cache layer. Use it.

/end 🧵

[Mutuku Makau @drmakau]

@AuroraMar1eL Good but insufficient.

[Mutuku Makau @drmakau]

@KenyaPower_Care Please look into complaint #14902278 which is being "fixed" since yesterday. And poor customer service assistance.

[Mutuku Makau @drmakau]

@elonmusk @NdindiNyoro Stated the same about where Kenya should focus.

[Mutuku Makau @drmakau]

@nderi_j While it carries little credit risk (banks rarely default on overnight loans to each other), it carries massive liquidity risk. In a debt-distressed economy, the "price" of liquidity is never free of risk. After a year plot Tbill rate and interbank rate) over time.

[Mutuku Makau @drmakau]

@nderi_j Further interest rates shouldn’t be allowed to increase by more than 2% annually.

[Mutuku Makau @drmakau]

@nderi_j While efficient in stable markets, KESONIA strips away borrower protections. By pegging loans to volatile overnight rates, it ensures that government fiscal mismanagement immediately hits households and businesses.

[Mutuku Makau @drmakau]

@nderi_j The base rate is calculated by looking at the interest rate banks pay on customer deposits (savings accounts, fixed deposits), not just what they charge each other.

[Mutuku Makau @drmakau]

@nderi_j The @CBKKenya should consider alternatives that protect SMEs, youth, and citizens using less volatile means such as 90 day rolling average of the interbank rate OR even better: Cost of funds index.

[Mutuku Makau @drmakau]

@EAukot https://t.co/JAFzGFwM9d

[Tom Dörr @tom_doerr]

Open-source AI agent platform for financial analysis

https://t.co/8cK0RVT0n0

[Mutuku Makau @drmakau]

@ephraimnjegafan Assymetric fiscal consolidation.

[LangChain @LangChain]

🤖 🕸️ ScrapeCraft: AI-Powered Scraping

ScrapeCraft uses LangGraph to combine AI assistance with powerful web scraping. Features include bulk URL scraping, real-time data streaming, and AI-powered pipeline generation.

Explore the project on GitHub! 🚀
https://t.co/FXkM2RTygd

[Mutuku Makau @drmakau]

@MrKimmKE Opportunity for local manufacturing. Pap

[Mutuku Makau @drmakau]

@lordmiles @nzisakiilu The tech that allows you to post. NGITE

[Mutuku Makau @drmakau]

@keepersecurity I do not have a network connection issue