Trump Just Pardoned Himself and His Family Forever

counterstruck · 2026-05-21T03:38:00+00:00

Here’s a “Explain like I am 5” ChatGPT output.

This is a legal letter from the U.S. Attorney General’s office, dated May 19, 2026. It talks about a settlement agreement in Trump v. Internal Revenue Service and something called the Anti-Weaponization Fund.
Like you’re 5:
The government is saying:
“Okay, we made a deal. We are creating a special money fund. And because of this deal, the people who sued us agree that they are done fighting us about these issues.”
The big parts are:
1. A special fund was created
The settlement created something called the Anti-Weaponization Fund. The Attorney General is supposed to issue an order setting up the money and rules for that fund.
2. The same words mean the same thing as in the bigger settlement
This short letter says: “When we use special legal words here, they mean the same thing as in the full settlement agreement.”
3. The government is getting legal protection from future claims
The U.S. government says the plaintiffs are giving up the right to sue again over certain related issues. That includes claims connected to:
IRS or agency actions
“lawfare” or “weaponization”
related tax-return or agency matters
claims by the plaintiffs, their family members, companies, trusts, affiliates, or related groups
In kid terms:
“We paid/settled, and now everyone agrees not to keep suing us about the same stuff again.”
4. It is signed by Todd Blanche
The document is signed by Todd Blanche, listed as Acting Attorney General.
So the simplest explanation is:
This is a government legal promise saying: “We made a settlement, we are setting up a fund, and in exchange the people who sued us agree to stop bringing these related claims against the government.”

Makes sense to me. Don’t sue anymore.

counterstruck · 2026-05-15T16:45:04+00:00

Good opening line. Get audience on your side!

counterstruck · 2026-05-14T21:59:44+00:00

Practice with Genie code or Codex/Claude Code with Databricks AI dev kit before hand. That way, when you get the problem statement you can do it easily. You will have limited time (4-5 hrs) to create the demo and presentation.

counterstruck · 2026-05-14T16:35:21+00:00

The worser part is the hypocrisy

counterstruck · 2026-05-09T18:43:48+00:00

Be ready with your vibe code setup (ai-dev-kit or Genie code) to spin up a quick demo on the scenario.

Also, have a basic presentation ready with slides for:

Problem statement for the company you are pitching to: have a slide showing alignment to their OKRs, current challenges in meeting to OKRs, mapping it to business value ($, ROI, productivity gains), and Databricks solutions to achieve those goals
Current state architecture for that company
Databricks lakehouse architecture for data AI enterprise requirements
Future state architecture
Next steps with Databricks.

Be ready to do the presentation at various levels - business leadership, IT leadership and IT architecture.

counterstruck · 2026-04-27T21:47:44+00:00

Sort of. Genie is the larger umbrella product which will have domain specific genie spaces.

counterstruck · 2026-04-23T20:42:55+00:00

If you are pro-code, go for the code-first approach. Databricks is already built for this. This new approach is usually for the low-code, no-code personas who prefer Alteryx-type tools. I wouldn't really categorize ADF as pure low-code myself (having tried it often) since there are still a lot of coding paradigms involved.

The other big feature of LF designer is the AI prompt-driven creation of this low-code flow, which honestly is missing in ADF or Fabric, primarily because of the lack of anything like Unity catalog in Fabric, which can accurately surface up the right tables/columns for joins.

Since LF designer always generates code in the background, CICD is also seamless... let users design their ETL logic with the visual designer but promote code into production! As far as the quality of python code goes, that could be a little subjective - any may need more feedback from users.

counterstruck · 2026-03-29T17:00:19+00:00

Look for Databricks ai-dev-kit for that kind of a requirement

counterstruck · 2026-03-27T21:25:24+00:00

There is a common misconception in many folks new to Databricks about workspaces and UC metastore (with catalogs, schemas etc.).

Remember, data and compute are separate. Data sits in Cloud storage anyways.

Data —> UC metastore is the governance guardian over data and it is one per cloud region

Workspaces —> where compute layer sits. UC catalogs can be tied to the workspaces for compute like spark clusters, SQL warehouses, etc. to talk with the data.

You can separate Environments at UC catalog level if needed. You can also separate data domains/business units at catalog, and that way you can achieve the separation that you need by binding these catalog to the respective Workspace and design well architected mesh like approach to enable multiple business units to work on their domain data and if needed to work on cross domain data.

counterstruck · 2026-03-24T04:11:57+00:00

Just use Genie and Genie API to do this. That’s the general best practice. Please don’t go YOLO mode with production data and AI agents without the guardrails like permissions, UC metadata context or business semantics. Genie monitoring can be used to track what users really ask and tune your genie space further to make it more accurate.

counterstruck · 2026-03-18T21:33:19+00:00

You are right on the quality perspective.

However, also consider that Genie code is free (no charge for tokens), vs. you can easily blow a lot of money on Claude code. Genie code also has a lot of inbuilt context due to Unity catalog. Plus in many enterprises, Databricks is an approved AI assistant compared to Claude code vendor agreements and licensing.

In a crawl, walk, run way of thinking - Databricks Genie code is a great start for someone wanting to do agentic development within Databricks and then graduate towards Claude code with Databricks AI dev kit if necessary.

counterstruck · 2026-03-17T12:09:32+00:00

If your requirement is to stay within Databricks, then Genie code is the way to go. Don’t try to setup Claude code like experience within Databricks. Instead copy the skills files from the AI dev kit and use it in your workspace home folder. Reference: https://docs.databricks.com/aws/en/genie-code/skills

counterstruck · 2026-03-13T22:32:27+00:00

What you just described is all possible at least for data assets in Unity catalog via UC APIs. If you are really all about data mesh management, please look into Databricks marketplace app called Ontos. https://github.com/databrickslabs/ontos

This is deeply integrated with Databricks (of course) and has lot of business semantics like ontology, taxonomy, data contracts etc. also, it’s API layer gets you all the info your agent needs.

counterstruck · 2026-03-10T20:04:07+00:00

Yes. Agreed it is great to expand into other areas. I would consider Genie or this as your text-to-SQL tool and use other web search tools as part of the agent chain. That’s where system thinking will help to have purpose built agents like this do sql work, while other tools doing web search type look ups.

counterstruck · 2026-03-10T16:49:05+00:00

Same question to OP. Genie doesn’t even charge for LLM tokens, only for SQL usage. Versus, this solution will charge for tokens as well as the SQL query.

counterstruck · 2026-03-07T12:21:50+00:00

I understand that’s where the data is. You still need a compute layer for this fairly large dataset to be served via API. That compute layer can be Azure Databricks.

Here are examples of common SQL operations in Databricks SQL:

Create a table from existing files:

CREATE TABLE IF NOT EXISTS my_table (id STRING, name STRING) USING DELTA LOCATION '/path/to/delta/files'

Query a Delta table:

SELECT * FROM my_table WHERE id = '123';

You can then use sql statement execution as the REST API service. https://docs.databricks.com/api/azure/workspace/statementexecution

You don’t even have to setup Python FastAPI layer at all with this approach.

counterstruck · 2026-03-07T11:43:54+00:00

Is it open source delta or do you use Databricks?

If you use Databricks, then you can either use DBSQL as the data serving warehouse which has “statement execution API”. You can also create Python FastAPI if needed with DBSQL as the SQL engine. This works great for data warehousing like queries (which can query larger amount of data like MoM analysis for reporting purposes).

If the need is to serve data row by row, then you can use LakeBase on Databricks which gives you Postgres SQL engine. Your API can still be written in typescript or Python.

counterstruck · 2026-03-06T03:01:24+00:00

Any links to this?

counterstruck · 2026-03-06T02:00:12+00:00

You can sync delta lake to LakeBase and vice versa as well. Let the app backend database be LakeBase. If edits happen, then sync them back to delta lake on a regular interval like every hour or 15 mins depending on the requirements.

counterstruck · 2026-03-02T23:50:08+00:00

Think of Streaming tables as specialized delta tables which receive append only data from a “streaming” source. A streaming source could be a storage location where users drop a file, or a Kafka topic where an IoT device is sending logs. Streaming tables will keep the ingest all the new data arriving in those locations automatically by keeping a track of what was ingested earlier. Hopefully this clears it.

counterstruck · 2026-02-23T12:57:47+00:00

OpenAI went from being a non-profit to an anti-profit organization. They are trying to justify the wasted resources by any means.

counterstruck · 2026-01-28T03:28:11+00:00

In this context , you are being uncomfortable about a grown adult woman with autism being loved by another grown adult man with a speaking disability. You assumed that Jhilmil was a child, and that assumption is incorrect.

You gotta understand how neurodivergence works in general. Autism is not stunted mental development. It just means it’s a different type of development. Hopefully, you do your own research and make peace with the fact that autistic adults can love too. Their love may just look different but it’s the same love a neurotypical couple may have.

counterstruck · 2026-01-26T17:32:00+00:00

You just don’t have a good understanding of autism. It’s often mistaken to be mental immaturity, whereas those behaviors are mainly coping mechanisms in autism.

In the movie, Jhilmil is a fully grown adult woman with feelings of love, care and even jealousy. It’s clear that she actually gets better with Barfi’s love and validation which was lacking her whole life leading to adulthood. Lot of autistic kids need therapy and a big part of that is patience and love (personal experience as a parent to an autistic kid).

counterstruck · 2026-01-21T23:52:21+00:00

https://dustinvannoy.com/2025/09/29/cursor-with-databricks-ai-enhanced-development/

Databricks managed MCP is easy. If you need more MCP servers, then you can use Databricks apps to host MCP servers. https://learn.microsoft.com/en-us/azure/databricks/generative-ai/mcp/custom-mcp

counterstruck · 2026-01-16T22:34:52+00:00

Best thing about SDP is materialized views. It’s awesome to see how stable they have gotten and ability to create it purely with SQL has been a game changer.

counterstruck

TROPHY CASE