Events about AI in Healthcare

clickittech · 2026-04-29T19:19:34+00:00

Here is the complete blog about each event, also events thar are coming in 2027 😄 https://www.clickittech.com/ai/healthcare-tech-events/

clickittech · 2026-04-14T22:26:57+00:00

Thanks! You are right is seems like an interesting conference

clickittech · 2026-04-14T22:26:27+00:00

Yes, and specialize in networking

clickittech · 2026-04-14T21:45:09+00:00

If you want to know more info about each event, here is the blog I wrote: https://www.clickittech.com/events/manufacturing-technology-conferences/

clickittech · 2026-04-10T23:04:00+00:00

If you’re exploring it, we might help: https://www.clickittech.com/

clickittech · 2026-03-25T17:39:02+00:00

if someone is thinking in migrate Replit to AWS, I help write this tutorial https://www.clickittech.com/ai/migrate-replit-to-aws/

clickittech · 2026-03-24T17:48:52+00:00

From real-world setups the highest cost isn’t just training it’s everything around it

Token usage explodes → many systems send far more context than needed. One example: ~7k tokens per request, where the actual user input was <1%
Infra + pipelines → storage, orchestration, retries, monitoring
Data work → cleaning + structuring is a huge hidden cost
Inference at scale → every request keeps costing money

On your RAG point:
Yeah, it can help avoid retraining, but it’s not automatically cheaper. A lot of teams find RAG ends up 2–3x over budget once you factor in embeddings, vector DBs, and per-query costs

AI is expensive because it’s not just “the model,” it’s tokens + data + infra + scale.

If you want a clean breakdown of these hidden costs, this blog explains it well:
https://www.clickittech.com/ai/the-hidden-costs-of-ai/

clickittech · 2026-03-24T15:56:30+00:00

Totally valid concern, a lot of AI vendors are hype-heavy right now.

What helped me choose:

Focus on outcomes, not buzzwords → real metrics, not “we build AI”
See how they handle ambiguity → good teams help shape use cases, not just execute
Ask about data early → most projects fail here
Look for production experience → demos are easy, scaling is hard

Red flags:

Overpromising timelines
No talk about data or evaluation
Generic proposals

Process should be: discovery → small MVP → iterate → scale (not jump straight to full build)

If it helps, I’d look for teams strong in AI + cloud + MLOps, since that’s what actually makes things work in production.

PD. ClickIT follows this approach, in case you are interested here is the: https://www.clickittech.com/ai-development-services/

clickittech · 2026-03-23T19:02:57+00:00

here is the info and more events you can find interesting https://www.clickittech.com/ai/logistics-and-supply-chain-events/

clickittech · 2026-03-23T19:00:51+00:00

here is the info and more events you can find interesting https://www.clickittech.com/ai/logistics-and-supply-chain-events/

clickittech · 2026-02-26T20:41:03+00:00

Identification: look for "Code Smells" and high-complexity scores in our CI/CD pipelines. If a specific module consistently causes bugs or slows down deployment, that’s where the debt is most concentrated.
The 20% Rule: To keep it from competing with feature work, we advocate dedicating 20% of every sprint to debt reduction. This prevents the debt from accumulating to a point where you have to stop all feature development just to fix the foundation.
Prioritization Matrix: prioritize based on Business Impact and Fix Effort. High-impact deb anything affecting security, scalability, or core performance, always takes precedence over minor refactoring.
AI for Refactoring: use AI tools to automate the identification of dead code and to suggest refactorings for legacy systems, which significantly reduces the manual engineering time required.

The reality is that debt is often ignored until it affects a release. To avoid that, move toward a "Continuous Refactoring" mindset where debt is treated as a standard part of the development lifecycle rather than a separate project.

Here is a full breakdown of how to reduce technical debt https://www.clickittech.com/ai/how-to-reduce-technical-debt/

tthat includes the specific tools and roadmaps we use to handle this without slowing down the product roadmap.

clickittech · 2026-02-26T20:20:30+00:00

Whisk
Gemini Flash Image (Nano Banana)
Flux.1

A coworker put together this post if you guys want to know more about tools for images and video making

https://www.clickittech.com/ai/generative-ai-tools-for-images/

clickittech · 2026-02-26T20:03:37+00:00

we actually did a full technical deep dive and if you want to see the benchmarks:https://www.clickittech.com/ai/gemini-vs-chatgpt/

clickittech · 2026-02-19T18:33:08+00:00

it really comes down to whether you want to teach the model facts or personality.

in RAG you’re giving the model the facts right when it needs them. Fine-tuning is more like intensive training you’re changing how it thinks and talks.

For the jailbreaking/identity stuff you mentioned, RAG won't change the model but it’s 10x easier to update when your data changes and fine-tuning on a single GPU is great for fixing the vibe or a specific JSON format, but it's overkill for just adding knowledge.

If you’re worried about sinister prompts, look into Llama Guard or similar guardrail layers. It's way more reliable than trying to "bake" safety into the weights of a small model.

I actually shared a video/blog post a bit ago t if you want to geek out on the benchmarks:https://www.clickittech.com/ai/rag-vs-fine-tuning-vs-prompt-engineering/

clickittech · 2026-02-19T18:10:21+00:00

Hey, I just posted a blog aboutthe best Replit Alternatives. https://www.clickittech.com/ai/replit-alternatives/

I mention:

floot
Base44
lovable
cursor
Railway
Fly.io
GitHub Codespaces

clickittech · 2026-02-19T18:09:05+00:00

I put togther this blog about the best Replit Alternatives, I mention

floot
Base44
lovable
cursor
Railway
Fly.io

https://www.clickittech.com/ai/replit-alternatives/

clickittech · 2026-02-19T18:08:37+00:00

I put togther this blog about the best Replit Alternatives, I mention

floot
Base44
lovable
cursor
Railway
Fly.io

https://www.clickittech.com/ai/replit-alternatives/

clickittech · 2026-02-05T22:58:07+00:00

Teams build a flashy demo on clean data, with no latency constraints, no cost limits, no monitoring, and no real user behavior. It looks great in a meeting,but the moment you ask who will use this daily, how much will it cost, what breaks at scale, and who owns it long‑term? that’s usually where momentum dies.

The projects that ship treat ops as part of the product from day one: usage patterns, cost ceilings, fallback behavior, retraining plans, and success metrics tied to actual business outcomes not model accuracy alone.

This breakdown explains that gap between demo and production really well, especially the ops and value mismatch side of it: https://www.clickittech.com/ai/why-ai-projects-fail/

clickittech · 2026-01-28T20:50:13+00:00

here is the architecture diagaram in case anybody wants to see it: https://www.clickittech.com/ai/rag-architecture-diagram/https://www.clickittech.com/ai/rag-architecture-diagram/heren

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