grapine.ai

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[grapine.ai @grapine_ai]

I've been working on a couple of native #AgenticAI #Products and have been using #ClaudeCode quite a lot. There are two issues I saw for which i couldn't figure out in an easy way:

a. During claude code dev, the session fills up fast and I don't know what is all in there and how much is dead context in there. I usually have to clear the context which is bad for me.

b. During my Agentic loop using #langgraph (I prefer it), if there are many tool calls, the context again fills up quite quickly. Because of this, the LLM starts hallucinating and gives weird responses.

To solve this problem, I wrote a cli tool which can give a realtime view your claude code session and can actually mark/show which message can be removed safely.

npx @grapine.ai/contextprune watch

I've created an SDK for TS and Python which can compress your context in real time and you never face the problem of hallucinations even after 30 tool calls. It's a simple 1 line change in your code and it'll take care of your context automatically.

Python: pip install contextprune

TS: npm install @grapine.ai/contextprune

Examples:

TS: https://t.co/kuRvp2d5JZ

Python: https://t.co/3ljWK4wZsU

NOTE: it's all free to use. Everything stays on your machine, no data is sent anywhere!

https://t.co/sfdNMHkwdV

Do try it out and share your feedback!
#Context #Contextprune #hallucination #contextbloating #AI

[grapine.ai @grapine_ai]

Super interesting workflow. We've been experimenting with similar data joins; pulling county parcel records matched to business listings is surprisingly tricky when the owner's mailing address doesn't match the property. How are you handling those edge cases where the owner lives out of state, or the parcel record is outdated?

[grapine.ai @grapine_ai]

@jjude Anything you do manually today and that takes time is a good candidate. I have 5-7 jobs running every night.

[grapine.ai @grapine_ai]

@maverickecom UGC

[grapine.ai @grapine_ai]

@maverickecom Claude

[PromptSpar @PromptSpar]

1:1 prompt that produces small talk:

❌ "Help me prepare for my 1:1 with my manager."

1:1 prompt that gets you a promotion:

✅ "I have a 1:1 with my manager Friday. Last week I shipped [X], delayed [Y], blocked on [Z]. Goals this quarter: [A, B]. Help me prepare a 30-min agenda: 5 min wins, 10 min blockers + decisions needed, 10 min growth/feedback ask, 5 min strategic question I want their take on."

Managers reward prepared reports. Be one.

[PromptSpar @PromptSpar]

Hiring prompt that wastes 40 candidates:

❌ "Write 5 interview questions for a Senior PM role."

Hiring prompt that filters fast:

✅ "Write 5 interview questions for a Senior PM at a B2B SaaS, 200 employees. Each question must surface ONE specific signal, strategy, prioritization, communication, customer obsession, and technical depth. Include the answer pattern that signals 'hire' vs 'pass.' No leetcode-style questions."

Great interviews aren't trivia. Neither is a great prompt.

[PromptSpar @PromptSpar]

If your prompt works on the first try, you probably aimed too low.

[grapine.ai @grapine_ai]

@johnvirality AIUGC

[grapine.ai @grapine_ai]

@claudeai Time to give it a spin!

[PromptSpar @PromptSpar]

Prompt that gives you a textbook explanation:

❌ "Explain stock-based compensation to me."

Prompt that teaches you:

✅ "Explain SBC accounting like I'm a non-finance founder reviewing my first cap table. Use my actual numbers: 2M option pool, 4-year vest, 1-year cliff, 50 employees. Show how it hits the P&L vs cash. Flag the 2 things first-time founders get wrong."

Learning prompts work the same way as work prompts. Specificity unlocks everything.

[PromptSpar @PromptSpar]

Quick one for marketers:

Write a prompt that takes a competitor's landing page and extracts:
- The promise (above the fold)
- The proof (mid-page)
- The objection-handling (below the fold)

Now you have a teardown framework. Use it on 5 competitors. You'll see your own page differently.

[PromptSpar @PromptSpar]

Most prompt frameworks are 12 letters arranged into an acronym.

None of them help when you're staring at a blank box at 11 PM trying to write a prompt that actually works.

[PromptSpar @PromptSpar]

The phrase "AI is just a tool" is a tell.

It's said by people who haven't built the muscle to use it well.

A hammer is a tool. A leverage tool reshapes what's possible.

[grapine.ai @grapine_ai]

@LoganTGott Playbook

[grapine.ai @grapine_ai]

Six months back, I thought the hard problem in AI products was finding the right model.

The actual hard problems:
→ Prompt versioning across environments
→ Cost attribution at feature and user level
→ Reliable fallback routing when providers degrade
→ Observability across multi-step agent chains
→ Giving non-engineers control over AI behavior without eng involvement

None of these is a model problem. They're infrastructure problems.

Every mature engineering category had a moment where the infra layer got standardized, and teams stopped rebuilding it from scratch.

AI infrastructure is in that moment right now.

Building the layer that makes this the default. Watch this space.

[grapine.ai @grapine_ai]

The most underrated skill in AI engineering right now:

Knowing which tasks should NOT go to a frontier model.

GPT-4o for a classification task that a fine-tuned small model handles in 10ms at 1/50th the cost isn't impressive.

It's expensive laziness.

Model routing is where the real infra work is happening.

[grapine.ai @grapine_ai]

@JasonDigitl So true! I always believed in doing rather than reading/watching. Keep building, keep experimenting.

[grapine.ai @grapine_ai]

@neil_croy Yes, that's right. We did that exactly, have multiple agents but very scoped and limited to it's working, not general agents. They own only one task and that's it. Whenever something fails, it's easy to diagnose where it failed and why. How are you solving it?

[grapine.ai @grapine_ai]

Multi-agent systems expose every infrastructure weakness you've been ignoring.

Single agent: one call, one prompt, you see the failure.

Multi-agent: 12 sequential calls across 4 agents, one failure mid-chain, and you have no idea where it broke.

A thread 🧵

[JER @lifeofjer]

https://t.co/ofucbVgkLV

[grapine.ai @grapine_ai]

4/ This is the problem I'm spending most of my time on right now.

What observability stack are teams actually using for multi-agent in prod? Haven't found a great answer yet.

[grapine.ai @grapine_ai]

3/ If you're building agentic systems without this, you're not flying blind because things are working fine.

You just haven't hit a complex enough failure yet to know it.