Saffron Tech

Saffron Tech Saffron Tech is a well established company providing development services and IT solutions! Welcome to Saffron Tech's page!

We are thrilled to have you here and to share a little bit about who we are and what we do. At Saffron Tech, we are a team of seasoned professionals who are passionate about what we do. Founded by Vibhu Satpaul and Gaurav Sabharwal, with over 30 years of combined experience in the tech industry, we came to the scene with one goal in mind: complete digital transformation. With our market experience and your understanding of your vision, we bring a wealth of knowledge and expertise to the table that can be utilised for the realization of your goals. Our mission is simple: to help businesses of all sizes. Whether you are a startup or an established player looking to widen your goals, we help you leverage the latest technology to grow and succeed. We have the skills and know-how to take your business to the next level. With the multitude of options present in the industry, why choose Saffron Tech? First and foremost, we are committed to providing exceptional customer service. We know that navigating the world of technology can be daunting, and we are here to guide you every step of the way. Your goals become our goals and it is then that we create digital magic. We stay up-to-date with the latest trends and innovations in the industry, so you can rest assured that you are getting the most cutting-edge solutions available. From custom software development to web design and digital marketing, we offer a wide range of services to meet your unique needs. Thank you for visiting our page! We hope you'll take the time to learn more about Saffron Tech and how we can help your business thrive.

Google's Gemini 3.8 Live models can now handle asynchronous tool calls while continuing a spoken conversation.That creat...
31/10/2026

Google's Gemini 3.8 Live models can now handle asynchronous tool calls while continuing a spoken conversation.

That creates opportunities for voice applications that need to retrieve data or complete tasks without making every tool call feel like a long pause.

The engineering work becomes particularly interesting when the conversation crosses into a business system.

Take an order-status assistant.

The voice model needs to understand the spoken request, identify the correct order, retrieve current information, and communicate the result.

If the user interrupts halfway through, the application also needs to handle the unfinished response correctly.

Google's Live API documentation specifically describes clearing queued audio when a response is interrupted. It also explains session resumption when connections terminate.

Those details matter when choosing how the voice application will behave under real usage.

What would be hardest for your team to handle in a voice application: latency, interruptions, transcription accuracy, or system integration?

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GitHub's Spec Kit gives coding agents a structured way to move from requirements into implementation.Its workflow includ...
30/10/2026

GitHub's Spec Kit gives coding agents a structured way to move from requirements into implementation.

Its workflow includes specification, technical planning, task breakdown, implementation, and verification.

A useful place to start is with the decisions that change how the business process behaves.

Take refund approvals.

If the agent doesn't know who has approval authority or how duplicate refunds should be handled, it may implement technically valid behavior that violates the intended business rules.

For a small feature, a concise specification with acceptance tests may be enough. Larger changes may need additional planning and review.

The important part is keeping those decisions available when developers review the generated code.

Which requirement usually creates the most rework in your projects: business rules, permissions, exceptions, or acceptance criteria?

Claude Haiku 5.5 launched on October 7 with a focus on faster, lower-cost work, including coding subtasks.Anthropic also...
28/10/2026

Claude Haiku 5.5 launched on October 7 with a focus on faster, lower-cost work, including coding subtasks.

Anthropic also describes using smaller models alongside its larger models for different parts of a workflow.

That creates a useful experiment for engineering teams.

Take a repeatable development task and compare a single-model approach against a workflow that assigns repository exploration, implementation, and difficult reasoning separately.

Keep the acceptance criteria identical.

A cheaper model call only helps when the complete task still meets the required quality.

Track the time spent correcting outputs as well as the model usage cost.

Which coding task would you move to a smaller model first: repository search, test generation, documentation, or routine edits?

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Your application worked last week. Now a feature is broken, and several developers have merged changes since the last re...
27/10/2026

Your application worked last week. Now a feature is broken, and several developers have merged changes since the last release.

Git has a built-in command for investigating this: git bisect.

It uses binary search to narrow down the commit that introduced a reproducible bug.

Start with a failing version and a commit where the feature still worked.

Run:

git bisect start HEAD

Git checks out a commit between those two versions.

Test the feature.

If it works:
git bisect good

If it fails:
git bisect bad

Repeat until Git identifies the first bad commit.

When you're finished:
git bisect reset

For repeatable tests, Git also supports automating the process with git bisect run.

The important part is having a reliable way to reproduce the failure. If a historical commit cannot be tested, git bisect skip can exclude it from that check.

Finding the introducing commit gives engineers a much smaller change to investigate. The next step is understanding why it caused the regression and adding a test that catches the behavior.

When a release breaks, what usually takes longer for your team: reproducing the issue or finding the change responsible?

An API might respond quickly during local testing and become unreliable when many requests arrive together.Grafana k6 is...
26/10/2026

An API might respond quickly during local testing and become unreliable when many requests arrive together.

Grafana k6 is an open-source tool for testing that behavior with simulated traffic.

Once you have a valid test script and k6 installed, you can run:

k6 run --vus 100 --duration 30s script.js

That runs 100 virtual users for 30 seconds. Virtual users aren't a direct measure of 100 real people, because their request patterns depend on the script.

The results become more useful when you define acceptable performance beforehand.

For example, Grafana's documentation demonstrates thresholds for p95 response time and request failure rate. Teams should choose their own limits based on application requirements.

Also, a successful HTTP response doesn't always mean the business operation succeeded. Include checks for expected behavior.

Run load tests in an authorized environment where increased traffic and test data won't disrupt real users.

Which performance measurement creates the biggest release concern for your team: response time, error rate, or throughput?

Your support agent works correctly today.Next week, someone changes its prompt, model, or tool configuration.The origina...
24/10/2026

Your support agent works correctly today.

Next week, someone changes its prompt, model, or tool configuration.

The original test cases should still work.

Promptfoo is an open-source project that helps teams run repeatable evaluations against prompts and models.

You can try its official starter example:

npx promptfoo@latest init --example getting-started

Then, inside the generated project:

npx promptfoo@latest eval

Review the results with:

npx promptfoo@latest view

For a production support agent, build your own test cases around real behavior.

A customer asks for a refund. Another provides an incomplete account number. A third attempts to make the agent ignore its instructions.

Define what acceptable behavior looks like for each case.

Then rerun those cases when the application changes.

A passing evaluation doesn't guarantee every response will be correct. It gives engineering teams a repeatable way to identify known failures and investigate behavioral changes.

Which change causes the most unexpected behavior in your AI applications: prompts, models, tools, or retrieved data?

A company can build a document-search assistant on thousands of files and still struggle to retrieve the right informati...
23/10/2026

A company can build a document-search assistant on thousands of files and still struggle to retrieve the right information.

Sometimes the issue begins before the retrieval system sees the content.

Consider a financial report where the extracted text separates table values from their column headings.

The document has been converted, but the numbers have lost their meaning.

Two GitHub projects are worth evaluating here.

Microsoft MarkItDown converts common document formats into Markdown and is useful for straightforward text extraction.

Docling provides more detailed document parsing, including layout analysis, table structure, and OCR support.

Neither eliminates the need to check extraction quality. MarkItDown also supports OCR through a separate plugin.

Before indexing documents, inspect a sample containing tables, multiple columns, scanned pages, and complex formatting. Preserve document and page references in your indexing pipeline so answers can be checked against the source.

Which document type causes the most extraction problems in your AI projects: PDFs, spreadsheets, presentations, or scanned files?

A faulty invoice-processing workflow starts sending duplicate records to your ERP.Your engineering team disables the fea...
18/10/2026

A faulty invoice-processing workflow starts sending duplicate records to your ERP.

Your engineering team disables the feature flag.

New invoice requests stop entering the workflow.

But some invoices were already waiting in the queue, and others were being processed when the flag changed.

Those jobs may still reach the ERP.

The result depends on where the flag is checked and how background processing is controlled.

BullMQ, a popular open-source queue library, makes this distinction explicit.

Pausing a queue stops workers from picking up additional jobs. Jobs already being processed continue until they finish or fail.

That creates an important requirement for production automation.

A shutdown procedure should account for new intake, queued work, in-flight ex*****on, and actions already completed in external systems.

For financial workflows, idempotency controls also matter. Retried jobs should be designed to avoid creating duplicate business transactions.

And once an ERP record has been written, disabling the workflow won't reverse that action.

The useful test is to disable the automation while a job is actively processing, then verify exactly what stops and what completes.

Which part of your automation would be hardest to stop during an incident: queued work, active jobs, or external system writes?

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An API endpoint can pass every test your coding agent wrote and still fail on an unexpected input.Schemathesis gives eng...
17/10/2026

An API endpoint can pass every test your coding agent wrote and still fail on an unexpected input.

Schemathesis gives engineers a practical way to find those cases.

It reads your OpenAPI specification, generates requests, and checks responses for server errors, undocumented status codes, and schema violations.

Try its intentionally buggy demo API:

uvx schemathesis run https://example.schemathesis.io/openapi.json

For a local application with an OpenAPI file:

uvx schemathesis run ./openapi.yaml --url http://localhost:8000

You'll need the uv package manager. Run the tests in a safe development environment because generated requests can modify data.

One important limitation: schema validation won't catch every business-logic bug. Incorrect totals, duplicate transactions, and authorization decisions still need dedicated tests.

Which API failure tends to reach testing late in your projects: unexpected inputs, business rules, or authorization?

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A checkout bug can originate several steps away from where the customer notices it.A discount appearing twice might invo...
15/10/2026

A checkout bug can originate several steps away from where the customer notices it.

A discount appearing twice might involve the front-end event handler, an API retry, pricing logic, or a state update.

That makes the investigation process important.

One useful habit when working with coding agents is to separate verified findings from hypotheses.

Ask the agent to reference the actual function or file behind every suspected cause.

If it can't find evidence, the issue should remain an open hypothesis.

This makes the final implementation plan easier for another engineer to review and gives the team a record of why a particular fix was chosen.

Which part of debugging takes your team the longest: locating the code, tracing the request, reproducing the issue, or finding reliable tests?

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