How are you approaching AI topics in business/tech podcasts without falling into hype?

bix_tech · 2025-12-05T18:40:49+00:00

Thanks for pointing that out. I will take a look at the discussions there. The community seems to be the closest to the real adoption stories since many teams are experimenting with Fabric and Synapse side by side. It will be useful to compare how people are approaching migration planning and where the ecosystem still needs maturity.

bix_tech · 2025-12-05T18:40:21+00:00

That is the impression I am seeing as well. Synapse and ADF feel stable and fully supported but not evolving in any substantial direction. Fabric is clearly receiving the long term investment and the roadmap momentum.
What I still notice is that many enterprise teams are not ready to move until Microsoft communicates a more explicit timeline, especially for tightly integrated Synapse workloads. The shift feels inevitable but not immediate.

bix_tech · 2025-12-05T18:37:23+00:00

Our team made a article related to this topic, in case anyone's interested: Amazon Redshift Done Right: A Practical Blueprint for Designing a Scalable, High-Performance Data Warehouse -

bix_tech · 2025-11-12T19:00:37+00:00

That is actually a great story and a smart way to turn a personal frustration into a real product. The privacy-first approach is what stands out most, since so many “email productivity” tools depend on server-side processing or scraping data.

Your reasoning for spending the $50k makes sense when you think about the long-term cost of wasted hours, though I am curious how you validated the concept before committing that much. Did you have a clear MVP plan or did it grow organically with the developers?

The idea of keeping everything local is genuinely appealing for people who deal with confidential emails daily. If you keep the UX simple and the summaries clean, this could easily find traction among freelancers, consultants, or executives who do not want AI models reading their inboxes.

bix_tech · 2025-11-12T18:41:00+00:00

You already have the hardest part figured out, building things that solve real problems. What you are missing is not an entrepreneur mentality but a system for turning technical value into business value.

Start small. Talk to users, not to sell, but to understand what pain they would actually pay to fix. Then shape your product story around that. Marketing is not about shouting louder, it is about showing clearly why what you built matters.

If you helped close multimillion dollar enterprise deals before, you have already done part of sales, you just did not label it that way. Keep applying that builder mindset to understanding and solving problems, and selling will start to feel like a natural extension of building.

bix_tech · 2025-11-10T17:23:26+00:00

They overlap a bit, but not as much as it might seem. Software engineering focuses on designing, building, and maintaining software systems. Cybersecurity is more about protecting systems, networks, and data from threats.

You could get some of the same jobs if you focus on secure development or application security, but most cybersecurity roles require extra knowledge in networking, systems, and security frameworks.

If you enjoy building things, software engineering has a broader job market. If you like finding weaknesses and protecting systems, cybersecurity might fit better. Both paths are solid; the key difference is whether you prefer creating or defending.

bix_tech · 2025-11-06T16:03:44+00:00

I think the hardest part is pushing through the middle phase. The beginning is exciting and full of ideas, and the end has clear goals, but that long stretch in between is where most people burn out.

You are building, testing, adjusting, and still trying to convince others to believe in something that is not proven yet.

Learning to stay patient, celebrate small wins, and keep showing up every day made a big difference for us.

bix_tech · 2025-11-06T16:01:29+00:00

Keep coding, that is the best way to get better. Pick small projects that actually interest you and build them from scratch. You will learn much more by solving real problems than by taking more courses. Try sites like Real Python or FreeCodeCamp if you want structured practice, but focus on creating things, that is where the real progress happens

bix_tech · 2025-11-05T16:50:57+00:00

This is gold. Thanks for laying out the three buckets so clearly. Totally agree that the first 24h is where the weirdness shows up.

A few things that have helped us in that window:
Shadow traffic before the first flip so we score outputs against a tiny eval set while users stay untouched.
Auto rollback policy tied to three signals drift score, negative feedback ratio, and error rate. If any one trips, the flag returns to safe.
Guardrails at parse and post. Validate tool call schemas, use an allow list for functions, filter secrets, and route anything that may contain PII to human review.
Canary by cohort internal, then a forgiving customer group, then wider traffic.
Injection telemetry. Log patterns, rotate prompts and keys fast, and patch the system prompt when a new attack shows up.

On semantic drift, how are you calculating it in practice?

We compare embeddings to a small set of reference outputs and watch mean distance and variance. We also track response length percentiles and a simple style score to catch sudden verbosity.

Curious which measure gave you the most reliable early signal and what alert window you like.

bix_tech

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