Eric Hepperle

Full-Stack Designer & Creative Lead
📖 Author of Carnivore Chronicles: TeePublic Store:

[Eric Hepperle @EricHepperleWP]

Check out my latest LinkedIn post: https://t.co/E45C0LzWF8
#Affinity #Arial #BugReport #Typography #Windows11 #GraphicDesign #Troubleshooting #SoftwareBugs #DesignerLife #Canva

[unusual_whales @unusual_whales]

35% the Clarity Act is signed into law in 2026, per Polymarket:

[Eric Hepperle @EricHepperleWP]

My latest video: How I solved the palindrome with C#

https://t.co/TBWPdX2WIj

#csharp #dotnet #devLife #codingChallenge

[Eric Hepperle @EricHepperleWP]

@AnthropicAI #Claude used my entire tokens allotment and didn't create any version of the file I requested. Thumbs-down. Bad Claude. 2 months ago Claude was making my artifacts perfectly, in less than a turn.

[Eric Hepperle @EricHepperleWP]

#agentic #vscode

[Visual Studio Code @code]

⚡ Every token matters in agentic coding workflows.

Learn how the GitHub Copilot team is improving token efficiency in VS Code through smarter caching, tool search, WebSockets, and specialized subagents.

📖 Read the full post: https://t.co/NyCL0GZ3Ef

[Elon Musk @elonmusk]

Nashville getting drilled

[Neo Kim @systemdesignone]

If you want to elevate your AI engineering career (in 2026), save these 20 GitHub repositories:

1 OpenClaw
↳ Runs a personal AI agent locally that can browse, plan & take actions on your device.

2 TensorFlow
↳ Provides a production-ready framework to build, train & deploy machine learning models at scale.

3 AutoGPT
↳ Automates multi-step tasks by chaining LLM reasoning into autonomous agents.

4 n8n
↳ Automates workflows with a visual builder that integrates APIs, data & AI tools.

5 Ollama
↳ Runs open LLMs locally with simple commands & optimized performance.

6 Stable Diffusion WebUI
↳ Generates images locally with a powerful UI for Stable Diffusion models.

7 Hugging Face Transformers
↳ Offers thousands of pretrained models for NLP, vision & multimodal AI tasks.

8 Langflow
↳ Builds & tests LLM pipelines visually using a drag-and-drop interface.

9 Dify
↳ Creates production-ready AI apps with built-in orchestration, prompts & APIs.

10 LangChain
↳ Orchestrates LLM workflows, tools, memory & agents in applications.

11 Open WebUI
↳ Delivers a self-hosted ChatGPT-style interface with local & API model support.

12 DeepSeek-V3
↳ Provides a high-performance open-weight LLM optimized for reasoning and coding.

13 PyTorch
↳ Builds & trains deep learning models with flexible, research-friendly APIs.

14 Gemini CLI
↳ Interacts with Google’s Gemini models directly from the command line.

15 llama cpp
↳ Runs LLaMA-style models efficiently on CPUs & local hardware.

16 Whisper
↳ Transcribes & translates speech with high accuracy using deep learning.

17 ComfyUI
↳ Designs advanced image generation workflows using node-based pipelines.

18 CrewAI
↳ Coordinates multiple AI agents to collaborate on complex tasks.

19 RAGFlow
↳ Implements retrieval-augmented generation pipelines for enterprise search & QA.

20 Claude Code
↳ Assists coding with deep repository understanding & agent-style workflows.

What else should make this list?

===

💾 Save & RT to help others get good at AI engineering.
👤 Follow @systemdesignone + turn on notifications.

[Charly Wargnier @DataChaz]

DO YOURSELF A FAVOR: GO DOWNLOAD THIS NEW LOCAL MODEL AND KEEP IT IN STORAGE.

Even if you don't have a massive GPU setup, having offline access to an intelligent model is a crucial insurance policy.

Free API access won't necessarily last forever.

Right now, the 12B-27B range is the absolute sweet spot, and Hugging Models just highlighted a perfect candidate to download today:

→ GEMMA 4 12B CODER on @huggingface 🤗

It packs Google’s latest architecture into a GGUF format optimized for consumer hardware.

What it delivers locally:
→ Fast, private code completion without the cloud
→ Real-world debugging and reasoning capabilities
→ Smooth performance on 12GB+ VRAM or a standard CPU

Don't wait until you need it.

Grab the weights and keep them locally 👇

[Aizen @TechyAizen]

Me: The code works.

Tester:

[Eric Hepperle @EricHepperleWP]

#lunchBoxPC #AMD #TechNews

[Jokker @0xJokker]

AMD ACABA DE MATAR LAS SUSCRIPCIONES DE IA

La CEO de AMD Lisa Su presento oficialmente una PC del tamaño de una lonchera y ejecuto en vivo un modelo de 235 mil millones de parametros

Sin centro de datos. Sin nube. Sin GPU alquiladas

El chip en su interior es el AMD Ryzen AI Max+ 395

Es el primer chip x86 en el que la CPU y la GPU comparten el mismo bloque de memoria

Hasta 128 GB de memoria unificada

Una RTX 5090 te ofrece 32 GB de memoria de video

Una 4090 te da 24 GB

Pero esta pequeña maquina te ofrece mas de tres veces la memoria de cualquiera de ellas

Y cabe en una mochila

En inferencia con DeepSeek R1 le gano a una RTX 5080 por 3x

Una desktop del tamaño de un libro grueso superando una tarjeta grafica de mas de mil dolares en una carga de trabajo real de IA

Ahora haz las cuentas de tus suscripciones

Claude Code Max: $200 al mes
ChatGPT Pro: $200
Cursor: $20
Gemini: $20

Son $5,280 al año antes de construir una sola cosa

La version de 128GB de esta maquina cuesta entre $1,800 y $2,500

A ese ritmo se paga sola en menos de un año

Y despues corre sin costes adicionales, GRATIS

> Instalas Ollama
> Bajas Qwen3 235B
> Apuntas Claude Code a localhost
> La misma interfaz que ya usas
> Nada sale de tu maquina
> Nada cuesta por request
> Sin limitaciones a las 3am cuando por fin tienes tiempo para construir

Los abogados dejan de preocuparse por lo que OpenAI hace con sus archivos

Los developers dejan de ver el contador de tokens

Los founders dejan de matar prototipos porque la factura de la nube los asusta

La IA local ya no es solo una opcion mas economica

Es la unica IA que nadie puede quitarte

Y la pregunta ya no es si la IA local es lo suficientemente buena

Esta claro que si lo es

La verdadera pregunta es por que seguir pagando suscripciones cada mes cuando puedes correrla tu mismo

[Eric Hepperle @EricHepperleWP]

@josephabahx @KaiXCreator Just came here to drop a "me too"

[Cisco @FCisco95]

@KaiXCreator Claude code on CLI has 1M context still, using it right now (on other CLIs ofc)

[Tech with Mak @techNmak]

A dev got so frustrated watching his AI agent write 500 lines for a 5-line problem that he built a fix.

He called it Ponytail. Named after the guy every team has - long ponytail, oval glasses, been there longer than the version control. You show him fifty lines; he looks at them, says nothing, and replaces them with one.

Now your agent does the same. Before writing anything, it looks for a reason not to.

80-94% less code. 47-77% cheaper. 3-6x faster.

The best code is the code you never wrote.

GitHub Repo: https://t.co/WnFp9YNY53

[Eric Hepperle @EricHepperleWP]

#vscode #mermaid

[Visual Studio Code @code]

Mermaid diagrams are now built directly into @Code. 🎉
No extra extension required. 🙌

[ClaudeDevs @ClaudeDevs]

As a result of a US government directive, we are suspending access to Claude Fable 5 for all users. You can continue to use all other Claude models.

Here’s what this means for you:

Across Claude products, new sessions will run on your selected default model or Opus 4.8, and existing Fable 5 sessions will end with an error.

On the Claude Platform, requests to Fable 5 will also return an error. Please update your integrations to other Claude models.

We know this is a disruption to your workflows; we appreciate your patience and support.

[Ann Coulter @AnnCoulter]

This is huge. What were all those other Republican presidents doing???

[Eric Hepperle @EricHepperleWP]

Interesting!

[Neo Kim @systemdesignone]

If you want to get ahead of 99% of software engineers, learn these system design concepts:

1 Scalability
2 Availability
3 Reliability
4 Latency
5 Throughput
6 Capacity
7 Client-Server
8 Database
9 SQL vs NoSQL
10 Load Balancing
11 Caching
12 Cache Invalidation
13 CDN
14 DNS
15 API Design
16 REST
17 GraphQL
18 gRPC
19 Authentication
20 Authorization
21 Rate Limiting
22 Fault Tolerance
23 High Availability
24 CAP Theorem
25 Consistency Models
26 Replication
27 Partitioning
28 Sharding
29 Indexing
30 Denormalization
31 ACID
32 BASE
33 Microservices
34 Monolith
35 Event-Driven
36 Message Queue
37 Pub/Sub
38 Sync vs Async
39 Idempotency
40 Backpressure
41 Circuit Breaker
42 Bulkhead
43 Retry Logic
44 Timeout
45 Service Discovery
46 API Gateway
47 Load Shedding
48 Autoscaling
49 Blue-Green
50 Canary Release
51 Feature Flags
52 Observability
53 Logging
54 Metrics
55 Tracing
56 Correlation ID
57 Monitoring
58 Alerting
59 Full-Text Search
60 Time Series
61 Vector DB
62 Materialized View
63 Query Optimization
64 Connection Pooling
65 Cache Stampede
66 Cache Warming
67 CDN Caching
68 Data Compression
69 Serialization
70 Deserialization
71 WebSockets
72 WebRTC
73 CQRS
74 Event Sourcing
75 Service Mesh
76 Sidecar
77 BFF
78 Strangler Pattern
79 LSM Trees
80 B-Trees
81 Merkle Trees
82 Bloom Filter
83 HyperLogLog
84 MapReduce
85 Batch Processing
86 Stream Processing
87 ETL
88 Data Pipeline
89 Data Lake
90 Data Warehouse
91 Secrets Management
92 RBAC
93 SSO
94 Encryption
95 Checksum
96 Erasure Coding
97 Consensus
98 Leader Election

...and 16 more concepts.

===

💾 Save & RT to help others master system design.
👤 Follow @systemdesignone + turn on notifications.

[Elon Musk @elonmusk]

The reason ID is banned in California (and New York) elections is to enable large-scale fraud.

When you combine no ID and mail-in voting, fraud is de facto legalized.

[Manuel Sampedro @manuelsampedrop]

@MasterTimBlais The frontier-lab diaspora is going to look like the PayPal mafia in five years. Labs compress a decade of learning into two, and the people leaving now know exactly which problems are still unsolved. The solo-lab cohort is the one to watch this cycle.

[𝐇𝐚𝐦𝐳𝐚 | Networking Guy @Hamzaonchain]

COMPONENTS OF DOCKER EXPLAINED (Simplified)

Docker is a containerization platform that packages apps so they run consistently across environments. Its core components are:

1. Docker Images
Read-only blueprints used to create containers. They include the app, runtime, libraries, dependencies, and configs. Images are typically stored and shared via registries like Docker Hub.

2. Docker Containers
A running instance of an image. Containers are lightweight, isolated, and portable environments that ensure applications behave consistently anywhere they run.

3. Dockerfile
A script that defines how an image is built. It specifies the base image, dependencies, environment variables, and build/run commands.

4. Docker Daemon (dockerd)
The background service that manages containers, images, networks, and volumes. It processes all Docker API requests.

5. Docker Engine
The full runtime system consisting of the daemon, REST API, and CLI. It’s what lets you build, run, and manage containers.

6. Docker Volumes
Persistent storage for containers. They keep data intact even if containers are stopped or deleted and can be shared across containers.

How it all fits together:
A Dockerfile builds an Image → an Image runs as a Container → the Docker Engine + Daemon manage everything → Volumes ensure data persistence.