The Git Times

The rasbt/LLMs-from-scratch repository, companion to Sebastian Raschka's book Build a Large Language Model (From Scratch), received its latest push on March 21, 2026. This update reinforces its role as a hands-on PyTorch guide for demystifying GPT-style architectures, now emphasizing code for loading and fine-tuning weights from larger pretrained models.

Developers familiar with the project know it walks through building a ChatGPT-like LLM from the ground up using Jupyter notebooks. It covers tokenization, embedding layers, transformer blocks, and autoregressive training—mirroring the scaled-up processes behind foundational models. The notebooks produce a small but functional model via pretraining on text corpora, followed by supervised fine-tuning for chat tasks.

What's evolved? Recent commits extend beyond toy models, integrating scripts to import weights from production-scale LLMs. This aligns with PyTorch's maturing ecosystem, where builders increasingly adapt giants like Llama or Mistral rather than train from zero. Raschka's approach uses standard components: multi-head self-attention, positional encodings, and causal masking, all implemented without external libraries beyond PyTorch.

# Example: Core GPT model skeleton
class GPT(nn.Module):
    def __init__(self, vocab_size, n_embd, n_head, n_layer):
        # Embeddings, transformer blocks, lm_head
    def forward(self, idx, targets=None):
        # Forward pass with logits

The repo stresses educational clarity: each notebook builds incrementally, with diagrams and explanations from the Manning-published book (ISBN 9781633437166). Clone via git clone --depth 1 https://github.com/rasbt/LLMs-from-scratch.git for the full suite, including setup tips for Python/PyTorch environments.

For builders, this matters now as closed-source LLMs dominate APIs. Understanding internals enables cost-effective fine-tuning on consumer GPUs, auditing black-box behaviors, or prototyping domain-specific models. It sidesteps library abstractions, revealing pain points like gradient accumulation for long sequences or LoRA adapters for efficiency—crucial for shipping reliable generative AI.

No fluff: if you're debugging transformer convergence or customizing attention mechanisms, these notebooks cut through hype. The update ensures compatibility with PyTorch 2.x features like torch.compile, keeping it relevant for production pipelines. Raschka's repo remains a benchmark for reproducible LLM education, proving small-scale experiments scale insights to enterprise.

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Newton's GPU-accelerated physics engine, built on NVIDIA Warp, continues to evolve for roboticists tackling complex simulations. Last updated in March 2026, the project—initiated by Disney Research, Google DeepMind, and NVIDIA—now emphasizes extensible, differentiable computations via its integration of MuJoCo Warp as the primary backend. This extends Warp's deprecated warp.sim module, adding OpenUSD support for scalable scene handling and user-defined extensions for custom forces or contacts.

What sets Newton apart is its focus on GPU-native performance without local CUDA Toolkit installs. On NVIDIA GPUs (Maxwell+ with driver 545+), it delivers rapid iteration for robotics workflows. Python 3.10+ users on Linux, Windows, or macOS (CPU-only fallback) can start with:

pip install "newton[examples]"
python -m newton.examples.basic_pendulum

Recent pushes highlight robot-specific examples, including robot_g1 and robot_h1, enabling quick tests of humanoid dynamics like balance and locomotion. Basic demos cover URDF imports (basic_urdf), joint chains (basic_joints), conveyors (basic_conveyor), and heightfields (basic_heightfield). Advanced features include recording/replay (recording, replay_viewer) and plotting for analysis.

Differentiability shines for reinforcement learning pipelines, where gradients flow through physics for policy optimization. OpenUSD integration streamlines asset pipelines from tools like Omniverse, while extensibility lets researchers override solvers—crucial for novel actuators or hybrid systems.

As a Linux Foundation project under Apache-2.0, Newton fosters community maintenance, with docs under CC-BY-4.0. Install from source via uv for bleeding-edge tweaks.

For builders, Newton solves the bottleneck of CPU-limited sims in robotics R&D. Its Warp foundation ensures 100x+ speedups over traditional engines, making it ideal for scaling to fleets of virtual robots. Why now? Surging humanoid projects demand fast, gradable sims—Newton delivers without vendor lock-in.

Key technical edges:

• No-CUDA setup: Warp handles kernels remotely.
• Cross-platform: Full GPU on Linux/Windows, CPU on macOS.
• Extensible APIs: Inject custom collision or integration logic.

Roboticists iterating on RL or control should benchmark against these updates—Newton's momentum positions it as a Warp successor for production sims.

The edoardottt/awesome-hacker-search-engines repository, a staple for security practitioners, received its latest push on March 21, 2026, expanding its curated roster of specialized search engines. This update arrives as developers and builders face intensified pressure to integrate reconnaissance into CI/CD pipelines and vulnerability scanning—making quick access to niche tools more critical than ever.

At its core, the list addresses a key pain point in penetration testing and threat hunting: sifting through the internet's vast attack surface without drowning in general-purpose engines like Google or Bing. It organizes over 100 resources into targeted categories, from servers and vulnerabilities to emerging areas like surveillance cameras and crypto.

Server-focused engines dominate early sections. Shodan maps the Internet of Things, exposing unsecured devices from industrial controls to webcams. Censys and Onyphe.io provide deeper certificate and exposure data, while FOFA and Quake enable cyberspace-wide queries for protocol-specific fingerprints. Builders scripting automated scans can pipe these into shell workflows—the repo's primary language—for scalable asset discovery.

Vulnerabilities get robust treatment: NIST NVD and MITRE CVE for official disclosures, GitHub Advisory Database for ecosystem-specific alerts, and Vulners.com for cross-referenced exploits. Newer additions like osv.dev streamline open-source vuln tracking, vital for devs maintaining supply chains.

The update shines in niche expansions. Crypto tools hunt blockchain leaks, People searches aggregate social/OSINT profiles, and Surveillance cameras point to Insecam-style indexes—timely amid IoT breach spikes. Threat Intelligence covers GreyNoise for noise filtering and Natlas for network scaling.

For builders, this isn't passive reading: integrate via shell one-liners, e.g., querying ZoomEye APIs in recon scripts or Hunter for email recon. Red/Blue teams save hours; bug bounty hunters accelerate scoping.

Why now? With state-sponsored ops leaning on OSINT, and tools like these feeding ML-driven threat models, the refresh ensures parity against 2026's evasion tactics. No bloat—just signal for secure coding and ops.

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The base/node repository, a staple for self-hosting Base—an Optimism OP Stack L2—has issued v0.14.9, flagged as a recommended update specifically for Base Geth nodes. This release incorporates op-geth v1.101609.1 (from ethereum-optimism/op-geth) and op-node v1.16.7 (from ethereum-optimism/optimism), addressing recent chain stability and performance needs. Full changelogs detail the diffs, underscoring incremental fixes vital for production syncing.

Running a Base node via this repo sidesteps centralized RPC reliance, enabling builders to verify transactions, index data, or expose custom endpoints on mainnet or Sepolia testnet. Setup leverages Docker Compose: configure L1 endpoints like OP_NODE_L1_ETH_RPC, OP_NODE_L1_BEACON, and OP_NODE_L1_BEACON_ARCHIVER in .env.mainnet or .env.sepolia, then docker compose up --build. For testnet, prefix with NETWORK_ENV=.env.sepolia.

Supported clients include reth (default, with optional Flashblocks via RETH_FB_WEBSOCKET_URL), geth, and nethermind. Switch via CLIENT=reth docker compose up --build. Minimum hardware demands 32GB RAM (64GB advised), multicore CPU, and NVMe SSD with storage for 2x chain size plus snapshots and buffer. Production mirrors AWS i7i.12xlarge instances with RAID 0 NVMe/ext4 for Reth archive or Geth full nodes.

This update matters now as Base's TVL climbs and dApp activity surges, amplifying risks of third-party RPC downtime. Geth operators, in particular, gain from op-geth's targeted patches, ensuring sync parity with L1 Ethereum. Reth users benefit indirectly via stack-wide op-node improvements.

For builders, v0.14.9 lowers barriers to L2 node sovereignty. DeFi protocols can validate settlements independently; indexers avoid rate limits; and RPC providers scale without vendor lock-in. With Base's low-cost, secure design, self-hosting aligns with Ethereum's decentralization ethos, especially post-Dencun upgrades stressing L2 resilience.

The repo's three-year maturity reflects battle-tested Docker orchestration atop OP Stack, but this release sharpens its edge for Geth-heavy setups amid evolving L1-L2 interplay.

OpenWeedLocator (OWL), the Raspberry Pi-powered weed detection system, has marked a significant evolution with its first stable release, v1.0.0. Long a staple for DIY precision agriculture, OWL now fully supports the Raspberry Pi 5 and picamera2, alongside legacy picamera setups. A new config/config.ini file streamlines tuning detection parameters, making green-on-brown algorithms more adaptable for in-crop and fallow fields.

At its core, OWL processes images from off-the-shelf cameras to identify weeds via simple thresholding—readily upgradable for advanced models. Detection triggers a relay board or custom driver to activate 12V solenoids, relays, or sprayers. The system packages into 3D-printable or extruded aluminum enclosures, with fresh compact designs released in May 2024. Files are available directly from the repo or Printables.

Recent momentum underscores OWL's staying power. Last pushed in March 2026, the project launched a dedicated Discourse forum in December 2025 for agtech discussions, complementing GitHub Issues. A full YouTube installation guide dropped in February 2025, and the biweekly OpenSourceAg Newsletter keeps builders looped in. These resources lower the barrier for integrating OWL on robots, vehicles, or bikes.

The v1.0 release, DOI-stamped via Zenodo for research citation, invites community pull requests to refine code and hardware. Bugs fixed and features polished mean deployers can trust it for site-specific spraying without proprietary lock-in.

For builders tackling herbicide reduction, OWL delivers low-cost autonomy: under $100 in parts, Python-based, and extensible. It sidesteps heavy ML dependencies, prioritizing reliability in dusty fields. Researchers and smallholders gain a forkable baseline; the Pi 5 upgrade boosts edge performance for real-time decisions.

Why now? As ag faces regulatory pressure on chemical use, OWL's refinements position it for broader adoption in spot-treatment fleets. Check the Discussion tab for mounting hacks and parameter tweaks—it's builder-driven evolution in action.