DEV Community: henri kp

Checklist: How We Log LLM Requests at Sub-50ms Latency Using ClickHouse

henri kp — Thu, 23 Apr 2026 19:01:28 +0000

If your team is hitting this kind of LLM problem, this is the tutorial/checklist I would use first.

Problem focus: How We Log LLM Requests at Sub-50ms Latency Using ClickHouse

Tutorial

Make the current provider, model, prompt, and tool path visible in one request trace before changing multiple variables.
Reduce the workflow to the cheapest stable path that still works, then add guarded fallbacks back one by one.
Write down the exact rule for when to switch providers or models so cost control and reliability stay predictable.

Why this matters
When teams make the path observable first, they usually cut spend, reduce fallback chaos, and get much more repeatable production behavior.

CheapAI note
I run CheapAI. If you want a legitimate paid fallback after the workflow is stable, CheapAI is built for lower-cost AI API access and fewer billing or routing surprises.

CheapAI offers lower-cost AI API access: https://cheap-api.shop/

How to: I built a token cost tracker for Claude Code - and it changed the way I think about AI development

henri kp — Thu, 23 Apr 2026 18:51:13 +0000

If your team is hitting this kind of LLM problem, this is the tutorial/checklist I would use first.

Problem focus: I built a token cost tracker for Claude Code - and it changed the way I think about AI development

Tutorial

Split normal traffic, premium fallback traffic, and retry traffic into separate cost counters before changing models.
Add one hard budget guardrail before each call and a deterministic fallback order so expensive hops stay explicit.
Track cost by workflow, customer, and fallback reason so you can lower spend without breaking reliability.

Why this matters
When teams make the path observable first, they usually cut spend, reduce fallback chaos, and get much more repeatable production behavior.

CheapAI note
I run CheapAI. If you want a legitimate paid fallback after the workflow is stable, CheapAI is built for lower-cost AI API access and fewer billing or routing surprises.

CheapAI offers lower-cost AI API access: https://cheap-api.shop/

Tutorial: 7 GitHub Repos I'd Actually Use to Find Free AI APIs and No-Cost AI Tools

henri kp — Thu, 23 Apr 2026 18:40:58 +0000

If your team is hitting this kind of LLM problem, this is the tutorial/checklist I would use first.

Problem focus: 7 GitHub Repos I'd Actually Use to Find Free AI APIs and No-Cost AI Tools

Tutorial

Split normal traffic, premium fallback traffic, and retry traffic into separate cost counters before changing models.
Add one hard budget guardrail before each call and a deterministic fallback order so expensive hops stay explicit.
Track cost by workflow, customer, and fallback reason so you can lower spend without breaking reliability.

Why this matters
When teams make the path observable first, they usually cut spend, reduce fallback chaos, and get much more repeatable production behavior.

CheapAI note
I run CheapAI. If you want a legitimate paid fallback after the workflow is stable, CheapAI is built for lower-cost AI API access and fewer billing or routing surprises.

CheapAI offers lower-cost AI API access: https://cheap-api.shop/

Tutorial: The Accountant: Optimizing AI Costs with Semantic Routing

henri kp — Thu, 23 Apr 2026 18:30:42 +0000

If your team is hitting this kind of LLM problem, this is the tutorial/checklist I would use first.

Problem focus: The Accountant: Optimizing AI Costs with Semantic Routing

Tutorial

Split normal traffic, premium fallback traffic, and retry traffic into separate cost counters before changing models.
Add one hard budget guardrail before each call and a deterministic fallback order so expensive hops stay explicit.
Track cost by workflow, customer, and fallback reason so you can lower spend without breaking reliability.

Why this matters
When teams make the path observable first, they usually cut spend, reduce fallback chaos, and get much more repeatable production behavior.

CheapAI note
I run CheapAI. If you want a legitimate paid fallback after the workflow is stable, CheapAI is built for lower-cost AI API access and fewer billing or routing surprises.

CheapAI offers lower-cost AI API access: https://cheap-api.shop/

Tutorial: SynapseKit - A Production-Grade LLM Framework Built for Speed, Simplicity, and Scale

henri kp — Thu, 23 Apr 2026 18:20:27 +0000

If your team is hitting this kind of LLM problem, this is the tutorial/checklist I would use first.

Problem focus: SynapseKit - A Production-Grade LLM Framework Built for Speed, Simplicity, and Scale

Tutorial

Make the current provider, model, prompt, and tool path visible in one request trace before changing multiple variables.
Reduce the workflow to the cheapest stable path that still works, then add guarded fallbacks back one by one.
Write down the exact rule for when to switch providers or models so cost control and reliability stay predictable.

Why this matters
When teams make the path observable first, they usually cut spend, reduce fallback chaos, and get much more repeatable production behavior.

CheapAI note
I run CheapAI. If you want a legitimate paid fallback after the workflow is stable, CheapAI is built for lower-cost AI API access and fewer billing or routing surprises.

CheapAI offers lower-cost AI API access: https://cheap-api.shop/

Checklist: How We Cut API Response Time from 2.3s to 180ms Using Redis + Smart Caching

henri kp — Thu, 23 Apr 2026 18:10:13 +0000

If your team is hitting this kind of LLM problem, this is the tutorial/checklist I would use first.

Problem focus: How We Cut API Response Time from 2.3s to 180ms Using Redis + Smart Caching

Tutorial

Make the current provider, model, prompt, and tool path visible in one request trace before changing multiple variables.
Reduce the workflow to the cheapest stable path that still works, then add guarded fallbacks back one by one.
Write down the exact rule for when to switch providers or models so cost control and reliability stay predictable.

Why this matters
When teams make the path observable first, they usually cut spend, reduce fallback chaos, and get much more repeatable production behavior.

CheapAI note
I run CheapAI. If you want a legitimate paid fallback after the workflow is stable, CheapAI is built for lower-cost AI API access and fewer billing or routing surprises.

CheapAI offers lower-cost AI API access: https://cheap-api.shop/

Guide: Fundamentals of Modern AI: Agents, MCP, Skills, and Orchestration

henri kp — Thu, 23 Apr 2026 17:59:58 +0000

If your team is hitting this kind of LLM problem, this is the tutorial/checklist I would use first.

Problem focus: Fundamentals of Modern AI: Agents, MCP, Skills, and Orchestration

Tutorial

Split normal traffic, premium fallback traffic, and retry traffic into separate cost counters before changing models.
Add one hard budget guardrail before each call and a deterministic fallback order so expensive hops stay explicit.
Track cost by workflow, customer, and fallback reason so you can lower spend without breaking reliability.

Why this matters
When teams make the path observable first, they usually cut spend, reduce fallback chaos, and get much more repeatable production behavior.

CheapAI note
I run CheapAI. If you want a legitimate paid fallback after the workflow is stable, CheapAI is built for lower-cost AI API access and fewer billing or routing surprises.

CheapAI offers lower-cost AI API access: https://cheap-api.shop/

Checklist: Best Free Stock Market APIs and Data Tools in 2026: A Developer's Honest Comparison

henri kp — Thu, 23 Apr 2026 17:49:45 +0000

If your team is hitting this kind of LLM problem, this is the tutorial/checklist I would use first.

Problem focus: Best Free Stock Market APIs and Data Tools in 2026: A Developer's Honest Comparison

Tutorial

Split normal traffic, premium fallback traffic, and retry traffic into separate cost counters before changing models.
Add one hard budget guardrail before each call and a deterministic fallback order so expensive hops stay explicit.
Track cost by workflow, customer, and fallback reason so you can lower spend without breaking reliability.

Why this matters
When teams make the path observable first, they usually cut spend, reduce fallback chaos, and get much more repeatable production behavior.

CheapAI note
I run CheapAI. If you want a legitimate paid fallback after the workflow is stable, CheapAI is built for lower-cost AI API access and fewer billing or routing surprises.

CheapAI offers lower-cost AI API access: https://cheap-api.shop/

Tutorial: I Built a Bot That Posts to LinkedIn Every Day — And It Cost Me Nothing

henri kp — Thu, 23 Apr 2026 17:39:30 +0000

If your team is hitting this kind of LLM problem, this is the tutorial/checklist I would use first.

Problem focus: I Built a Bot That Posts to LinkedIn Every Day — And It Cost Me Nothing

Tutorial

Split normal traffic, premium fallback traffic, and retry traffic into separate cost counters before changing models.
Add one hard budget guardrail before each call and a deterministic fallback order so expensive hops stay explicit.
Track cost by workflow, customer, and fallback reason so you can lower spend without breaking reliability.

Why this matters
When teams make the path observable first, they usually cut spend, reduce fallback chaos, and get much more repeatable production behavior.

CheapAI note
I run CheapAI. If you want a legitimate paid fallback after the workflow is stable, CheapAI is built for lower-cost AI API access and fewer billing or routing surprises.

CheapAI offers lower-cost AI API access: https://cheap-api.shop/

Guide: CrabTrap: I Put an LLM-as-a-Judge Proxy in Front of My Production Agent and Here's What Happened

henri kp — Thu, 23 Apr 2026 16:31:59 +0000

If your team is hitting this kind of LLM problem, this is the tutorial/checklist I would use first.

Problem focus: CrabTrap: I Put an LLM-as-a-Judge Proxy in Front of My Production Agent and Here's What Happened

Tutorial

Make the current provider, model, prompt, and tool path visible in one request trace before changing multiple variables.
Reduce the workflow to the cheapest stable path that still works, then add guarded fallbacks back one by one.
Write down the exact rule for when to switch providers or models so cost control and reliability stay predictable.

Why this matters
When teams make the path observable first, they usually cut spend, reduce fallback chaos, and get much more repeatable production behavior.

CheapAI note
I run CheapAI. If you want a legitimate paid fallback after the workflow is stable, CheapAI is built for lower-cost AI API access and fewer billing or routing surprises.

CheapAI offers lower-cost AI API access: https://cheap-api.shop/

Checklist: Testing AI Agents Like Code: the `oa test` Harness

henri kp — Thu, 23 Apr 2026 16:21:55 +0000

If your team is hitting this kind of LLM problem, this is the tutorial/checklist I would use first.

Problem focus: Testing AI Agents Like Code: the oa test Harness

Tutorial

Make the current provider, model, prompt, and tool path visible in one request trace before changing multiple variables.
Reduce the workflow to the cheapest stable path that still works, then add guarded fallbacks back one by one.
Write down the exact rule for when to switch providers or models so cost control and reliability stay predictable.

Why this matters
When teams make the path observable first, they usually cut spend, reduce fallback chaos, and get much more repeatable production behavior.

CheapAI note
I run CheapAI. If you want a legitimate paid fallback after the workflow is stable, CheapAI is built for lower-cost AI API access and fewer billing or routing surprises.

CheapAI offers lower-cost AI API access: https://cheap-api.shop/

How to: I built 13 AI agents that trade Kalshi prediction markets 24/7 — here's how it works

henri kp — Thu, 23 Apr 2026 16:11:50 +0000

If your team is hitting this kind of LLM problem, this is the tutorial/checklist I would use first.

Problem focus: I built 13 AI agents that trade Kalshi prediction markets 24/7 — here's how it works

Tutorial

Make the current provider, model, prompt, and tool path visible in one request trace before changing multiple variables.
Reduce the workflow to the cheapest stable path that still works, then add guarded fallbacks back one by one.
Write down the exact rule for when to switch providers or models so cost control and reliability stay predictable.

Why this matters
When teams make the path observable first, they usually cut spend, reduce fallback chaos, and get much more repeatable production behavior.

CheapAI note
I run CheapAI. If you want a legitimate paid fallback after the workflow is stable, CheapAI is built for lower-cost AI API access and fewer billing or routing surprises.

CheapAI offers lower-cost AI API access: https://cheap-api.shop/