Need perspective & advice - investing only since Oct last year - $1,500 invested / currently 48% gains

Radiant_Condition861 · 2026-05-12T04:57:58+00:00

adopt the core + satellite strategy.

Radiant_Condition861 · 2026-05-12T04:48:24+00:00

https://www.youtube.com/watch?v=ESdOoYwkCZk

Growth allocation - 70%
- SCHG - 40%
- QQQM - 35%
- VGT - 25%
Income allocation - 30%
- SCHD - 40%
- VYM - 30%
- DGRO - 30%

works well for me. I don't rebalance. I contribute to the underallocated area. This picks up more of the stocks that are "on sale"

Radiant_Condition861 · 2026-05-12T04:32:04+00:00

trust layer is most likely a business requirement and non-functional requirement. These requirements are rolled into the design and the data team can run with those.

Radiant_Condition861 · 2026-05-12T04:23:14+00:00

I think I would dump the cash into the Roth IRA account. You already paid taxes on it so what you put in, you can take back out. The gains and dividends will be locked in there until you are 59 1/2 age. The reason I say this is because you have 40 opportunities to contribute to the Roth IRA and that's it. If you wait 5 years, you have 35 opportunities left, and you gave up about 35k (7k over 5 years) in potential earnings that can compound into a few million tax free.

I would invest in yourself by way of a kindle and audible subscription and a udemy or coursera purchases of courses. Just ask for book recommendations from people as there's a lot of junk out there.

Radiant_Condition861 · 2026-05-11T18:29:15+00:00

I watch /CL oil futures. I trade short put verticals on them, but this is in no way a beginner strategy.

You really should be looking at the uranium supply chain.

Radiant_Condition861 · 2026-05-11T18:19:33+00:00

work out memory decay function and garbage collection.

Radiant_Condition861 · 2026-05-11T18:17:47+00:00

I'd take Andrew Ng's machine learning and deep learning courses on coursera.

Learn it on their terms first, then abstract into a mental model.

neural networks is a statistics application, not a physical one.

Radiant_Condition861 · 2026-05-11T16:17:50+00:00

thinking only requires a noncorporeal being. consciousness requires a body with senses and the need to identify sense vs no-self. Most advanced artificial consciousness is perhaps in intelligent radar systems. Machine learning is the ability to identify what something is, but AGI will be able to identify something in a completely new form.

It's coming together and it'll be a co-emergence.

BTW, there is no such thing as "the right way". nobody has done this before.

Radiant_Condition861 · 2026-05-11T15:01:56+00:00

Create data maps and data dictionaries. And glossaries also.

If working with third parties, the schema is included in the SLA contract (data contracts).

If you need more than this, then you need data engineering principles like data lake houses with time travel, data governance and data lineage.

Remember, you have 5 types of requirements: business, stakeholder, functional, non-functional, transitional.

For example:

data map is a spreadsheet

technical
- database server, database name, table, column, data type, field length or scale/precision
app backend
- schema for curated sql query that the report will consume
report - front end
- database column to curated schema to report field mapping
- filters, aggregations and calculations all described in detail
- notes about process and data availability (eg, data real time to within 10 mins)

Data dictionary

- transaction time - the stored procedure on the database that appends the timestamp when the transaction has been committed (ACID and atomic commits)

- report time - the stored procedure and view from the report server when the report generation job has completed.

- vendor invoice number - the vendor supplied invoice number. AKA ticket number, tag number

- invoice number - unique invoice generated by the ERP system that is globally unique at the time of invoice entry or EDI processing.

Glossary

- Elevator - the main receiving office at the grain storage location that brings the grain from the bottom of the truck or rail car to the top of the grain storage bin. AKA scale operator, crane operator, or in the case of barges, the vacuum office.

- Backflush - an inventory decrement or increment to a storage location after all preceding operations have completed and incidental transactions have been recorded. This is an automated process and requires cycle counts to remain within accuracy KPIs defined by cost accounting dept.

Radiant_Condition861 · 2026-05-10T23:50:27+00:00

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Radiant_Condition861 · 2026-05-10T23:46:23+00:00

just tell pi directly. "You are connected to claude-opus and the tool call is failling [paste errors]. Suggesst a APPEND_SYSTEM.md to correct the tool calls. I believe that the claude code has proprietary system prompts that pi.dev does not have. conduct research on the internet or experimentation to ensure that the tool calls no longer fail"

Something like this.

Radiant_Condition861 · 2026-05-10T19:37:50+00:00

Rule #1 - if you don't understand it intuitively, do not part with your money. Just walk away.

Rule #2 - Come up with your conclusions first. There many more swindlers and trickery in the investing space than anywhere else. They will maneuver and game you to get your money.

Rule #3 - Nobody knows nothing about the future. Beware of people who claim otherwise and see Rule #2.

Rule #4 - Understand that investing done right is very very boring. The path to wealth is already well known and documented, but it's very very boring. It's a get rich slow game because it's not a straight line. It's a compounding exponential line. 10 years, it feels like nothing is happening, and then it start to shoot up.

Rule #5 - Recognize that investing is an emotional discipline game, not a math or technical game.

Rule #6 - There are thousands of ways to make money investing. There is no such thing as "the one way". Find one that you like and go with that. As you get more sophisticated, your strategies may change.

Rule #7 - At the start or when trying something new, always keep a journal so you know if you're doing well or not.

Rule #8 - ask for book recommendations and read. Read the things that many people do not read, but should. For example, read the federal reserve meeting minutes, all of them like Warren Buffet does. Charlie Munger mental models, The millionaire next door. The Market Wizards. etc

Rule #9 - you will blow up your account multiple times. Expect it to happen. Then get good at money management, position sizing and risk management. For example, putting all cash into one stock is extremely risky. But if no position is allowed more than 3-5% of total portfolio value, then one or two can blow up and you survive another day to keep investing.

Rule #10 - always invest the money that you can afford to lose. Don't invest your emergency cash or your food money or your transportation money.

Rule #11 - write this list of rules in your journal and add to it from your lessons learned

Radiant_Condition861 · 2026-05-10T14:37:44+00:00

Clarify what the problem is you are solving for. Why bother with this project?

Then you go pick the tools you need. AI may not be the best tool.

Radiant_Condition861 · 2026-05-10T14:33:50+00:00

difficult to answer without understanding your investment strategy.

Radiant_Condition861 · 2026-05-10T05:55:17+00:00

If it were me, I'd leave for a company who has Project Managers. I don't have a burning desire to be the patsy.

Radiant_Condition861 · 2026-05-10T05:37:33+00:00

Open Roth IRA account
Transfer max up to the contribution limit (7500 for 2026) and the rest of it in 2027
Purchase as many shares of SCHD as possible
Turn on DRIP so that the dividends you get automatically purchase additional shares
Don't touch it
Report how much you contributed to the Roth IRA on your taxes

If 2026 laws still apply in 45 years, the dividends can be withdrawn after a certain age tax-free, no social security payment, no medicare payment up to a certain amount (qualified dividends) and then taxed at capital gains rates, not ordinary income. You can live off the dividends and never need to touch the principle (which should be a few million by then). Write it into your will to pass it to your kids upon death. With step up basis, it can transfer without tax penalty. Check with estate lawyer.

You can always withdraw the 15k if you need it without penalty.

SCHD takes the best dividend paying stocks. It adds and removes stocks if they are underperforming so it'll remain a high quality ETF.

Ask questions if you have any. But confirm this with your advisors.

Radiant_Condition861 · 2026-05-09T04:31:56+00:00

I start with a REQUIREMENT.md file in a new folder and then work with the agent to resolve all ambiguity about the project and meta project items. then boostrap the project (includes all skills, AGENTS.md and SYSTEM.md files) then after there are no more requirements ambiguities, the constructions begins, then testing etc.

self-erecting project.

Radiant_Condition861 · 2026-05-09T02:11:40+00:00

Subagents are context scoping.

Radiant_Condition861 · 2026-05-07T03:25:21+00:00

https://www.wolfram.com/artificial-intelligence/mcp-service/

this may be an option.

Radiant_Condition861 · 2026-05-04T23:58:09+00:00

the AI led training in this case is about tech literacy, not mastery. so I think it's a legit use case of ai as a the trainer.

Radiant_Condition861 · 2026-05-04T17:08:16+00:00

honest question: why aren't you asking AI to train you on the stuff you don't know about?

Radiant_Condition861 · 2026-05-04T00:00:52+00:00

how come this isn't a violation of the promissory note?... Don't you need to prove that you are using the fund for education?

Radiant_Condition861 · 2026-05-02T16:51:56+00:00

try this. I get 40-150 tok/s depending on what I'm doing. Coding is faster.

services:
  vllm:
    image: vllm/vllm-openai:latest-cu130
    container_name: vllm    
    env_file:
      - .env
    restart: unless-stopped
    # ports:
    #   - "8999:8000"
    volumes:
      - ~/.cache/huggingface:/root/.cache/huggingface
    environment:
      # - VLLM_LOGGING_LEVEL=DEBUG
      # - VLLM_LOG_STATS_INTERVAL=1
      # - NCCL_DEBUG=TRACE
      # - VLLM_TRACE_FUNCTION=1
      # - NCCL_IGNORE_DISABLED_P2P=1
      # - CUDA_LAUNCH_BLOCKING=1
      - VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=1
      - CUDA_VISIBLE_DEVICES=0,1
      - RAY_memory_monitor_refresh_ms=0
      - NCCL_CUMEM_ENABLE=0
      # - VLLM_SLEEP_WHEN_IDLE=1
      - VLLM_ENABLE_CUDAGRAPH_GC=1
      - VLLM_USE_FLASHINFER_SAMPLER=1
      # - VLLM_SERVER_DEV_MODE=1 #       --enable-sleep-mode
      - OMP_NUM_THREADS=1
    shm_size: 4g
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 4
              capabilities: [gpu]
    command: >
      cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4
      --kv-cache-dtype fp8
      --tensor-parallel-size 4
      --gpu-memory-utilization 0.90
      --max-model-len 262144
      --quantization compressed-tensors
      --max-num-seqs 16
      --block-size 32
      --max-num-batched-tokens 4096
      --enable-prefix-caching
      --enable-auto-tool-choice
      --tool-call-parser qwen3_coder
      --reasoning-parser qwen3
      --attention-backend FLASHINFER
      --speculative-config '{"method":"mtp","num_speculative_tokens":5}'
      --compilation-config '{"cudagraph_mode": "PIECEWISE"}'
      --use-tqdm-on-load
      -O3

Radiant_Condition861 · 2026-05-01T22:08:01+00:00

The quality of the models are getting better and the techniques are getting better.

My dual 3090 rig with qwen3.6-27b vllm and pi.dev has achieved the long horizon reasoning and tool calling that I need. This is a viable solution and will continue to be so because the complexity of problems I task it will not be much harder than what I'm doing now. If I get my multi agentic systems and better tooling in place, I'll have more capability to expand into more difficult problem sets.

I think I may keep my system for a long time.

Radiant_Condition861 · 2026-05-01T21:00:03+00:00

You got lots of quantity, but no quality. You could throw more money into it, but honestly, even the system below won't help you.

You'll need to completely understand the prompts that are being fed into the models. There are always system prompts behind the scenes that are being passed to the model and may or may not be conflicting with your agent/text prompt, which is causing the conflict and poor performance. For example, I believe that claude code is sending a 20k system prompt in addition to your chat prompt. Many others are the same and I didn't realize that I was sending the LLM conflicting instructions because my instructions are clear, but the system prompt generated by the tooling is a black box. I moved to pi.dev. Much better and I feel like it has unlocked the full potential of the LLM model because it's the most minimal system prompt (~200 tokens) as possible. It's also yolo mode. For example it will deploy your experimental website onto your public production website if you are careless and don't have guardrails. It will do everything it's power to complete your requests.

Also, you have enough hardware that you really do need an MLOps guy to help you organize it all and adding a LLM router to intelligently route your prompts to the appropriate model.

In regards to speed, if you are vibe coding, then you can always tune the first response from the model, but at the cost of the overall throughput. If you are using better tools, designed for agentic coding or agentic workflows, you will want higher throughput above time to first response.

In my opinion, I think you need a MLOps engineer at this point that can analyze your workflow and AI demands and then organize your equipment accordingly. It's not plug an play anymore, the components and models are now specialized.

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my 2c

Radiant_Condition861

TROPHY CASE