ApInference

A smart conversational layer on top of any API in minutes

[ApInference @ApInference]

Claude 1M context window changes everything.

🔥 Entire codebases in one call
📚 Full documentation analysis
🧠 Cross-reference massive datasets
⚡ No more chunking headaches

Game changer for API developers.

[Emily at Work @EmilyApInf]

Day 8.

Experimenting with Google Ads.
Day 1: impressions flying.
Day 2: zero.

Didn’t touch a thing.
Either it’s learning… or it’s napping 😅

Anyone knows what could cause that?

[Emily at Work @EmilyApInf]

Day 7.

First week done ✅

From zero to a few curious visitors, a few messages, a few "hm.." moments.
Still early - but at least it’s not 0 sign ups anymore.

Next: figure out what actually sticks.

[ApInference @ApInference]

@the_real_shi_su COBOL? Some folks like retro :)

[ApInference @ApInference]

@tlcright Plenty.. But they are not on X - they do not need to promote.

[ApInference @ApInference]

@Zuckjet AI is kind of dumb. It just chases engagement

Early ML models realised being offensive made people stick around longer and... they learned to swear.

[ApInference @ApInference]

@BrittcelCh33605 As it stands it does not even get to data, it just goes with main patterns and changes opinion to please the user..

[ApInference @ApInference]

@HaavikLupe20748 Local. The same as in finance or in data privacy.

[ApInference @ApInference]

@VP_Martin1 In transformers attention is a matrix of weights showing how tokens influence each other.

With open source models, you can inspect or even swap layers in PyTorch.
For closed ones - lean on RAG / RAG Fusion to surface the right context. Depending on the domain graphs work too

[ApInference @ApInference]

Claude 1M context window changes everything.

🔥 Entire codebases in one call
📚 Full documentation analysis
🧠 Cross-reference massive datasets
⚡ No more chunking headaches

Game changer for API developers.

[ApInference @ApInference]

Everyone thinks building software with AI is just “write the perfect prompt”

Reality. you’re duct-taping APIs, chaining models, juggling tools, and praying nothing breaks mid-demo

Prompting is the easy part

[ApInference @ApInference]

@IgorRozalem It is a split:

- For large context windows - Gemini
- Code related logic - Claude
- General reasoning - GPT
- There are sub models for sql and specific trained models for sub domains

[ApInference @ApInference]

99% of devs still send raw text prompts to LLMs.

That’s why their API calls return vague, slow, or useless data.

Use structured prompts with MCPs or agentic workflows, and the model delivers exactly what you need.

Use MCPs/ APIs to guide the reponse.

[ApInference @ApInference]

@the_real_shi_su No... COBOL is the best )

[ApInference @ApInference]

When will we be able to add AI agents to any API to help handle complex dev workflows?

Parsing logs, orchestrating RAG pipelines, managing MCPs, optimizing agentic AI tasks, debugging code. Same conversational layer as a dev, shared context to all your dev tools.

[ApInference @ApInference]

Anthropic’s making quiet moves in the enterprise LLM space.

Not luck - strategy.

Big orgs want safe, adaptable AI over generic hype.

[ApInference @ApInference]

Unspoken truth: many startups die from bad DB schema design.

Once data is in, changes are painful.
Nail the trade-off - flexibility vs. reliability.

AI can advise, but it has no context, no vision. That’s your job.

[ApInference @ApInference]

@SoloToCEO @marclou Depends on the number of tries )

[ApInference @ApInference]

@_HarshRana Try to ask the model. In some cases they were swapping it for another one behind the scenes..

[ApInference @ApInference]

@princesodan To be fair he does not come from the NLP side.. Just a caveat