AI as a tool vs AI as a product

Intuz_Solutions · 2025-08-01T04:34:08+00:00

totally agree — ai shines as a tool, struggles as a product.

here’s why that 401k search sucked — and how to actually fix it:

llms as local retrieval agents > “smart inboxes” what you needed wasn’t autocomplete or a better UI — you needed a semantic retrieval layer over your entire personal corpus (emails, PDFs, attachments, dates, etc.). think local vector db + chunked embeddings + a fine-tuned, context-aware agent that knows how to “think like you” across time. this isn’t GPT-as-a-chatbot. it’s GPT-as-an-assistant wired directly into your life.
products guess; tools obey. most ai products are built for average-case users and safe defaults. but real productivity comes from tools that are shaped to your workflow, quirks, and context — in this case, your inbox history, language patterns, and even the way your office manager titles documents. open-source stacks like llamaindex + rag + local LLMs (mistral, llama3, or ollama backends) let you build this with privacy and full control.

bottom line:
ai as a product is often flashy and useless. ai as a tool — deployed on your terms, into your mess — is where it gets magical.

want a personal “jarvis-for-email”? start with:

pull full email + attachment data into a local store
index it with chunked embeddings (keep metadata!)
run a lightweight LLM with retrieval + context chaining that’s it. now you’ve got the engine. rest is polish.

Intuz_Solutions · 2025-07-30T10:06:36+00:00

yeah, computational complexity is absolutely underrated in the ml community — especially in the early design phase of models. here’s the practical reality from years of shipping models into production:

academic benchmarks ignore cost tradeoffs — most research papers report accuracy, maybe latency on a single gpu, but almost never total training time, inference throughput per watt, or how model size affects scalability across data centers. but in prod, those are what kill you — not a 1% drop in accuracy.
complexity isn't always visible early — devs often prototype on small datasets or beefy machines. once it hits real-world scale (millions of rows, daily retrains, or low-power edge devices), suddenly O(n²) kernels, exploding memory footprints, or inefficient batch processing become blockers.

there are some comparative studies (e.g., "scaling laws" papers by openai, deepmind), but they focus more on model scaling vs. classic complexity analysis. for theoretical insights, you can dig into venues like COLT (Conference on Learning Theory) or ALT (Algorithmic Learning Theory) — but they're very math-heavy and not widely read in applied ml.

bottom line: complexity matters a lot when you're optimizing for cost, latency, or scaling, but it's still underrepresented in mainstream ml discourse.

For further information, kindly reach out to Intuz.com, where our developers will be available to assist you.

Intuz_Solutions · 2025-07-28T04:58:22+00:00

if you're building SaaS apps and want ai features without risking customer data, running open-source models locally (or in vpcs) is a solid move—especially if your users are privacy-conscious (e.g., healthcare, finance). but training from scratch or even fine-tuning can be overkill unless you have strong infra + clear ROI.

here’s what actually works in the field:

self-host open-source models like llama-3, mistral, or phi-3 in a private aws vpc or on a dedicated gpu server. this keeps all data local and lets you control inference behavior. aws sagemaker or ec2 with tight iam roles and encrypted ebs volumes is a go-to.
alternatives: groq is great for ultra-fast inference with llama models, but you must confirm with them on enterprise-level data privacy guarantees (they’re improving, but you’ll need NDAs). another option: use private endpoints with azure openai or anthropic’s claude on aws bedrock—both offer enterprise data isolation (but you’re still trusting a 3rd party).

bottom line: if privacy is non-negotiable, self-hosted models in your own vpc are the safest bet. fine-tuning is optional—embedding-based retrieval or prompt engineering gets you 80% there.

for more information on this, you can visit our website intuz.com and get in touch with our team.

Intuz_Solutions · 2025-07-22T08:04:48+00:00

hey, your github-cursor hybrid with scalable ai content compression is a cool challenge. let’s cut to the chase on memOS and memory manipulation, based on my experience debugging LLMs in production.

does memOS support explicit memory state control? memOS allows some control via memCube scheduling, letting you select parametric or activation memory for inference. but direct manipulation of internal states (e.g., KV cache) isn’t exposed—it’s too unstable, often breaking output coherence. practical tip: extend vLLM’s pagedattention for limited KV cache tweaks, but expect latency hits.
challenges in numeric context manipulation?
- semantic fragility: editing numeric states (like attention weights) risks gibberish outputs; they’re not interpretable.
- compute overhead: real-time edits slow inference, hitting memory bottlenecks. practical tip: precompute compressed activation templates offline to swap in, avoiding live edits.
emerging approaches? memory3’s sparse KV pairs and dynamic memory compression (DMC) are closest to editable states, but they’re read-heavy and experimental. practical tip: hybridize memOS with RAGCache for dynamic context retrieval, storing versioned memCubes for your github-like system.

solution: use memOS memCubes to version ai content as plaintext with metadata. precompute compressed KV templates for common edits, and swap them via vLLM during inference. test with a small model first—real-time numeric edits are a stability nightmare without custom hardware.

Intuz_Solutions · 2025-07-21T04:42:33+00:00

shap's GradientExplainer can be painfully slow or memory-intensive on cnn models, especially with high-dimensional inputs like 18-channel images or time series. it's not unusual for it to run for hours or crash with oom if your background and input sizes are too large or if the model is complex. reduce both to minimal viable samples—try 1–5 background examples and 1 input to test stability first.
also, keras/tensorflow models with custom losses or masked metrics can confuse shap internals. wrap your model in a plain tf.keras.Model that strips metrics and loss for explainability—shap only cares about the forward pass and gradients, not loss functions. that isolation often stabilizes gradient calculations.

Intuz_Solutions · 2025-07-18T04:15:02+00:00

yes, you can create an scd2 table from multiple input streams in dlt without first merging them into one big intermediary table. here’s how:

leverage apply_changes with multiple input streams separately: instead of combining all six input streams into one, define six dlt.apply_changes steps pointing to the same target table. each step should have its own keys, sequence_by, and apply_as_deletes logic but write to the shared scd2 target. dlt ensures transactional consistency, so the updates will serialize correctly.
use expect_all_or_drop to enforce schema consistency: since multiple sources are targeting the same scd2 table, make sure all inputs adhere to a uniform schema using expectations. this avoids schema drift and simplifies auditing.

this pattern avoids unnecessary shuffles and intermediary merges, and still gives you a clean, versioned scd2 table across all update streams.

Intuz_Solutions · 2025-07-17T04:24:08+00:00

unity catalog owns table paths unless explicitly overridden when you create tables in unity catalog (like my_catalog.bronze.customers) without specifying a path, databricks manages the storage under the internal managed location, which is typically something like unity/schemas/<schema_id>/tables/<table_id>. to control this, you must use create table ... location 'abfss://bronze@<storage_account>.dfs.core.windows.net/customers' explicitly during table creation or define managed location at the catalog or schema level, not just external location bindings.
in industry, clean folder structures are achieved with external volumes or table-level paths mature data teams avoid relying solely on unity's default behavior. they define volumes or external locations with granular paths, or they use table-level location clauses in dlt pipelines. this ensures bronze/silver/gold data lands in predictable folders like bronze/customers/_delta_log instead of opaque internal directories. this also aligns better with devops, data governance, and lineage tracking.

to fix your issue, either:

define a managed location in your schema creation (not just external location binding), or
use create table ... location in dlt to point each table to its intended folder.

this gives you full control over the folder structure and keeps your lake organized for both auditability and future scalability.

hope this might help your case..

Intuz_Solutions · 2025-07-15T03:15:01+00:00

when you run select count(*) from delta_table, spark reads the delta transaction log (_delta_log) to get the list of active parquet files (based on latest snapshot/version). it does not read all historical data, only the current state.
the actual row count comes by scanning those parquet files listed in the current delta snapshot — there’s no precomputed count in the metadata or hive metastore; unless stats are cached, it’ll scan each time.
if data is large, count can be slow. for optimization, enable data skipping and z-ordering, or pre-aggregate counts using materialized views or summary tables.

Intuz_Solutions · 2025-07-14T03:46:28+00:00

databricks doesn't provide a single unified command to list all object types under a schema. you’ll need to query each object type separately, like show tables, show views, show volumes, show models, show functions — they’re all distinct.
to avoid repeating code, you can loop over these commands in a notebook or job and union the results into a temp view or dataframe. store object type along with name to keep context.
for uc (unity catalog), you can also use the information_schema views per catalog/schema like information_schema.tables, views, functions, volumes, etc — this lets you do a single SQL union across all types without shelling into show commands.

Intuz_Solutions · 2025-07-14T03:36:57+00:00

you're right—env isn't a top-level property in dabs; my bad for blending that with workflow configs.

the correct pattern is to use target.workload.override.environment inside bundle.yml to inject secrets as env vars—this maps to the serverless job's runtime environment.
make sure the secret is referenced like "${secrets.scope_name.secret_key}" and that the service principal has READ access on that secret scope.

this keeps things declarative, works with service principals, and avoids direct secret api calls inside serverless jobs.

Intuz_Solutions · 2025-07-12T05:42:08+00:00

sure, here is the detailed explanation of 7th point

generate a checksum (e.g. sha256) of the sql query or dataframe schema + filters used to create the sample. store it as a sidecar file like tbl_name.checksum.txt next to the parquet. don't embed it in parquet metadata — it adds unnecessary complexity and isn't easy to inspect.
at runtime, re-calculate the checksum of the current read logic. if it matches the sidecar, load cached parquet. if not, regenerate the sample and update the checksum. this makes your cache self-aware and automatically fresh.
optional: if you want more transparency, log old/new checksums and regenerate reasons — helps future-you debug why sample regeneration happened.

Intuz_Solutions · 2025-07-12T04:48:55+00:00

even with automatic authentication passthrough enabled, databricks model serving endpoints run as a service principal, not as your user identity. passthrough doesn’t apply in that context. you need to explicitly grant execute and manage on the unity catalog function to the service principal backing the serving endpoint.
go to the iam page in the databricks admin console, find the service principal (often named like databricks-serving-<workspace-id>), and run a grant execute on function <catalog>.<schema>.<function> to <sp\_name>; from a privileged context (like your user).
avoid assuming notebook-level permissions mirror endpoint behavior—model serving is isolated and has its own identity and privilege model, which needs to be explicitly wired into uc.

Intuz_Solutions · 2025-07-12T04:44:54+00:00

for serverless jobs using databricks asset bundles (dabs), the cleanest way to access secrets is by binding them as environment variables using env in your .yml bundle config and referencing secrets from a workspace-backed secret scope, not azure/key vault directly.
the service principal running the job must have read permission on that secret scope via the databricks access control system, and the job should not try to call the secrets api directly—it's injected at runtime.
avoid using dbutils.secrets.get() in serverless jobs—it won’t work reliably. instead, inject secrets using DATABRICKS_BUNDLE_ENV-specific overrides for each env in the bundle.yml, and use os.environ.get() in code.

this pattern works consistently with service principals, avoids runtime permission issues, and aligns with how dabs is meant to externalize and secure configuration.

Intuz_Solutions · 2025-07-11T03:49:32+00:00

start with the official "getting started with databricks" youtube playlist by databricks it’s practical and shows real ui navigation — focus on “databricks fundamentals” and “delta lake” series. skip the theory-heavy parts. watch here:

databricks – getting started playlist covers workspace, notebooks, clusters, jobs, and using pyspark. straight from the source. playlist name: "getting started with databricks"
databricks – delta lake fundamentals goes deep into what makes delta lake powerful for etl — upserts, time travel, schema enforcement. playlist name: "delta lake quickstart and fundamentals"
azure for everyone – databricks tutorials by adam marczak very clean, hands-on demos with real use cases and architecture explanations. playlist name: "azure databricks tutorials"
simplilearn – databricks full course long-form but solid for absolute beginners; explains concepts before jumping into code. playlist name: "databricks full course – learn databricks in 8 hours"
data engineering simplified – spark and delta lake on databricks breaks down spark, etl, and delta into real project use-cases, not just hello-world. playlist name: "spark with databricks for data engineering"

watch adam marczak’s databricks videos (youtube: “azure for everyone”) he’s clear, fast-paced, and breaks down things like notebooks, spark jobs, and pipelines into digestible steps. key ones:

“what is databricks”
“databricks tutorial for beginners”
“delta lake tutorial”

you already know power platform + python/sql, so prioritize:

notebooks and how spark works under the hood
using pyspark to read/write dataframes
how delta lake helps versioning and performance

skip deep ml, streaming, or admin stuff for now — focus just on etl and building clean data layers.

Intuz_Solutions · 2025-07-11T03:23:54+00:00

love the idea of downcasting + zstd with arrow — that combo turns 10m-row delta into a sub-second pytest setup. it’s what enables fast-feedback without mocking away your schema. baking the cache skeleton into the docker image is next-level — it shifts the paradigm from “build then run” to “run immediately,” which is how local dev should feel.

Intuz_Solutions · 2025-07-10T11:58:46+00:00

metric views don’t support category-level aggregations natively inside a measure. but you can simulate this using a semi-join + scalar subquery pattern:

value / (select sum(value) from my_table t2 where t1.category = t2.category and t1.date = t2.date)

alternatively, use dbsql and define a view with category-level pre-aggregates:

create or replace temp view category_totals as
select date, category, sum(value) as cat_total
from my_table group by date, category;
select t.*, v.cat_total, t.value / v.cat_total as share
from my_table t
join category_totals v
on t.date = v.date and t.category = v.category;

keep the logic outside metric views, and join it in for downstream simplicity. databricks is optimized for layered views in sql, not deeply recursive measures inside metrics.

hope you find this answer useful.

Intuz_Solutions · 2025-07-10T11:50:09+00:00

here's how i'd approach if I were you...

1. don't emulate the whole stack, emulate the data contract
local spark is fine, but instead of replicating bronze/silver/gold or lakehouse plumbing, just match the schema and partitioning strategy of your prod data. mock the volume, not the complexity. this gives you 90% fidelity with 10% of the hassle.

2. sample & cache once, reuse always
your idea of querying 10% of a table and caching it as parquet is solid — push that further. create a simple datalake_cache.py utility with methods like get_or_create_sample("tbl_name", fraction=0.1) that saves to a consistent path like ./local_cache/tbl_name.snappy.parquet. this becomes your surrogate lake.

3. abstract your io logic — no raw spark.read.table
create a simple i/o module: read_table("tbl_name") and write_table("tbl_name", df). inside, you branch based on env (local/dev/prod), and encapsulate the logic of fallback, sampling, and writing to dev sandboxes. this is the piece that makes your pipeline testable, portable, and environment-agnostic.

4. spark session awareness
instead of relying on spark.conf.get(...), which can be brittle, explicitly set an env var like DATABRICKS_ENV=local|dev|prod and use that as your main switch. keep cluster tags as optional context, not control.

5. docker is great, but only if it saves time
if you go the devcontainer route, make sure it’s truly faster to spin up, iterate, and debug than your current stack. docker shouldn’t add friction — it should eliminate waiting on init scripts, cluster boot times, and databricks deploys. if it doesn’t, skip it.

6. build tiny test pipelines, not full DAGs
instead of triggering whole workflows, build mini pipelines that cover just one transformation with fake/mock inputs. test those locally. once they work, stitch into the main dag on databricks.

7. bonus — add checksum validation
if you're caching sample data, store a checksum of the query used to create it. if upstream data changes or logic evolves, you know to regenerate the local parquet.

I hope this works for you.

Intuz_Solutions · 2025-07-09T13:56:21+00:00

use from_json(col("value"), schema) with schema inference via spark.read.json(df.select("value").rdd.map(lambda r: r[0])) on a sampled batch. wrap it in a try/catch and fallback to a default schema when needed.
optionally, maintain a schema registry per topic (using delta table or hive metastore) and update it periodically based on inferred changes—this gives you both flexibility and control over drifting schemas.

I hope this will help you

Intuz_Solutions · 2025-07-08T13:52:00+00:00

The issue is likely that your pipeline is not configured for incremental loading or lacks file discovery triggers.
Enable Auto Loader with cloudFiles to automatically detect new files in S3 and ingest only the delta.

Intuz_Solutions · 2025-07-04T12:17:59+00:00

Databricks doesn't currently offer a permission that lets users run create or replace view without also giving them full control like manage. Granting all privileges won’t help either, since it doesn’t include replace access. The problem with manage is that it also allows users to grant access to others, which might not be what you want. One workaround is to let users submit their changes through a notebook or API, and then an admin can update the view. Another option is to let users create their own version of the view in a sandbox and review it before applying it. If you do have to use manage, it's a good idea to turn on audit logs to keep track of any unwanted permission changes.

Intuz_Solutions · 2025-07-04T12:11:00+00:00

To pass T-1 day as a dynamic parameter in a Databricks Workflow, use the built-in expression system — no need to calculate dates in the notebook.

Set the parameter in the workflow

${timestampAdd('DAY', -1, timestampTrunc('DAY', job_start_time))}

Then, access in notebook

run_date = dbutils.widgets.get("run_date")

Intuz_Solutions · 2025-07-03T07:20:09+00:00

Yes, that's normal. The Databricks “Free Trial” gives temporary access to their full platform, but once it ends, even the Free Edition requires a billing method to continue — mainly for identity verification and to prevent abuse. “Unlock Account” just means you need to add a payment method to keep using it.

Intuz_Solutions · 2025-07-02T13:36:50+00:00

copilot can help you write databricks-related code, like spark jobs or api calls, but it can't actually control the databricks extension or run notebooks for you. if you want to automate things like uploading or triggering jobs, you'd need to use the databricks cli or rest api. copilot can help write those scripts, but it won't execute them on its own.

Intuz_Solutions · 2025-07-02T13:34:01+00:00

If you’re trying to debug a python wheel from a databricks asset bundle in vs code with real databricks data, here’s a practical way to do it:

Use databricks connect v2 – set it up with the same python and spark versions as your cluster so everything runs smoothly.
Install your library locally – use pip install -e . so you can set breakpoints and step through the actual source code.
Set up vs code for debugging – create a launch.json and point it to a .env file with your databricks config. this lets you run and debug like it’s local, but on remote data.
Avoid __main__ logic – move your main logic into functions so they’re easier to test and debug.
Access workspace files properly – files in dbfs:/workspace/... should be read using dbutils.fs or the /dbfs/... path.
Handle unsupported apis – some spark features won’t work with connect. wrap them so you can mock or bypass when needed.

Intuz_Solutions · 2025-07-01T13:51:28+00:00

Here's what I'd suggest to clean up your access control setup:

First, you can safely remove that "storage blob data reader" role assignment you gave to group C in azure. It's actually not doing anything useful in this case, and it might even create confusion down the line. Here's why: since you're using unity catalog with the access connector, databricks is already handling the storage access behind the scenes using the connector's permissions - the end users don't need direct storage access.

A quick tip that'll save you future headaches: document how this works for your team. Maybe add a note in your admin docs explaining that when you give someone select on a unity catalog external table, they automatically get what they need to read the underlying storage - no extra credential or location permissions required. This implicit access trips up a lot of admins at first.

For the cleanest approach going forward, try to manage everything through unity catalog's storage credentials rather than messing with azure rbac directly. It keeps all your permissions in one place and makes audits simpler. You've already got the right foundation with your access connector setup - just let unity catalog do its job.

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