ServerMO

Secure, Fast, Always On

[servermo]

How to Securely Deploy Supabase on ServerMO Dedicated Servers

Escape public cloud vendor lock in. Master secure Docker networking exact Nginx API routing and WebSocket persistence for Supabase on Server

Stop Raw-Dogging Your Supabase Deployments on the Public Internet

Let’s get one thing straight: if you just cloned the Supabase docker repo, typed docker compose up, and walked away, your infrastructure is fully compromised.

At ServerMO, we just dropped the SRE Masterclass on how to actually deploy self-hosted Supabase without giving hackers free rein over your Postgres database:

Docker Ignores Your Firewall: Yeah, UFW doesn't care if Docker tells iptables to open port 5432 to the whole planet. You have to go into the compose file and hard-bind Kong and the database strictly to 127.0.0.1.

The 404 SDK Nightmare: If you route your reverse proxy through a random /api/ folder, the Supabase client SDKs will break instantly. They expect root-level paths. Use exact Regex matching in Nginx.

Stop Killing Your WebSockets: Realtime subscriptions dying immediately? You forgot to pass the Upgrade headers in your Nginx proxy. Your proxy is literally suffocating the connection.

The "502 Bad Gateway" Reality Check: You can't run 15 massive microservices on a $5 shared VPS. The Linux OOM killer will silently execute your API gateway because it ran out of RAM. Get a real bare metal server with actual NVMe IOPS.

Secure your backend, keep your data, and stop paying insane cloud vendor fees.

[servermo]

How to Optimize MongoDB on Bare Metal Servers (SRE Guide)

Master MongoDB bare metal optimization. Prevent NUMA memory traps disable Transparent Huge Pages and enforce strict TLS encryption safely.

Stop Running MongoDB with Untuned Desktop Defaults

Let’s be completely honest: running an enterprise database pipeline with unoptimized cloud templates or default desktop Linux configs is a total disaster waiting to happen. You spend top dollar deploying lightning-fast bare-metal nodes, and then you leave standard kernel options active?

At ServerMO, we just released the absolute SRE Playbook to fix these structural configuration failures before they crash your production fleet:

The Multi-Socket Memory Slowdown: If you are running high-core dual AMD EPYC or Intel Xeon chips, your server uses Non-Uniform Memory Access (NUMA). Running MongoDB out-of-the-box traps allocations inside a single processor socket. Wrap your daemon via numactl --interleave=all to spread the load evenly across all available hardware pools.

The Transparent Huge Pages Timebomb: Linux likes handling memory in massive 2MB blocks via THP. MongoDB's WiredTiger engine works with tiny, granular allocations. Forcing them together triggers massive bloat, intense internal memory battles, and locks your server up permanently. Deactivate THP using a persistent boot daemon script.

Ditch the EXT4 Partition Standard: EXT4 chokes hard during the database engine's standard 60-second engine checkpoints under heavy write concurrency. Reformat your production NVMe database storage pools to XFS and eliminate write latency bottlenecks with explicit noatime parameters.

The Plaintext Security Illusion: A replica set cluster key file is NOT an encryption protocol. It’s just an identity pass. Relying on key files alone means your database passwords and raw document packets travel over internal switch lanes in completely vulnerable plaintext. Turn on strict requireTLS processing immediately.

Scale your data infrastructure cleanly without cloud licensing limits or resource throttling.

[servermo]

How to Safely Manage Linux Servers with CtrlOps (SRE Guide)

Automate Linux server troubleshooting with CtrlOps. Avoid cloud privacy leaks, root access traps, and establish Zero Trust port forwarding.

How to Safely Manage Linux Servers with CtrlOps

Let’s talk about the absolute danger of hooking up intelligent terminal agents directly to your server's root account.

Everyone loves the idea of agentless troubleshooting tools automatically scanning logs and spinning up remediation scripts. But if you connect that tool as root, and an exhausted system administrator accidentally clicks "approve" on a hallucinated code snippet at 3 AM? Congratulations, you just nuked your entire production environment.

At ServerMO, we just dropped the Enterprise SRE Playbook to prevent these exact infrastructure disasters:

Stop leaking corporate logs: Out of the box, CtrlOps can leak system metrics to commercial cloud providers. Run Ollama locally on an isolated Management Bastion so your laptop doesn't melt from thermal throttling.

The Ollama Hijack Trap: Ollama has ZERO native authentication. Exposing its port to the public internet turns your private server into a free-for-all public compute engine. Keep it bound to localhost and use encrypted SSH local port forwarding.

Fix your pipeline freezes: Agentless systems can’t type passwords. The moment your terminal runs a standard sudo command, the background prompt freezes the script forever. Harden your configurations with clear, targeted NOPASSWD directory entries for specific binaries.

Stop trying to replace Ansible: Intelligent terminals are imperative. Widespread multi-machine configuration management requires declarative frameworks (Ansible, Terraform) to stop massive configuration drift. Keep your terminals locked to rapid localized debugging.

Get real, unshared hardware performance without sacrificing operational privacy.

[servermo]

Virtualize Game Development with NVIDIA RTX PRO 6000 Blackwell Servers

Centralize studio workflows using NVIDIA RTX PRO 6000 Blackwell servers. Defeat hypervisor latency and Broadcom licensing taxes using Proxmo

Virtualize Game Development with NVIDIA RTX PRO 6000 Blackwell Servers

Let’s have a brutal engineering reality check about studio infrastructure.

If your game studio is still running multi-million dollar pipelines on physical workstations stashed under local office desks, you are burning money. But if you think buying a single 96GB NVIDIA Blackwell server means you can instantly dump 48 concurrent developers onto it because a marketing brochure said so, you're about to crash your entire pipeline.

That splits out to a miserable 2GB of VRAM per user. Unreal Engine will exception-crash before the landing screen even finishes loading. In the real world, you can fit 6 to 8 elite developers on that card. Maximum.

At ServerMO, our latest infrastructure blueprint exposes the raw mathematics of graphics virtualization:

The Broadcom Software Tax: Stop paying aggressive annual subscription fees on proprietary hypervisors just to avoid cloud egress. Run open-source Proxmox VE (KVM) or Red Hat OpenShift (KubeVirt) for raw, tax-free graphics compute paths.

The Viewport Streaming Paradox: Stop trying to stream viewports using consumer software that chokes on enterprise grid drivers and crashes your editors. Use certified protocols like HP Anyware (Teradici PCoIP).

The Noisy Neighbor Shader Crisis: For the love of clean frame pacing, implement strict NUMA node pinning so one dev compiling a 10,000-item shader doesn't drop the entire team's viewport down to a lagging 5 FPS.

Stop settling for underpowered hardware templates and rigid clouds that freeze your distribution pipelines.

[servermo]

How to Safely Run Claude Code on Ubuntu 24.04 (Enterprise SRE Guide)

Transform your dedicated server into a secure AI development environment. Master Podman isolation DBus safe systemd persistency and headless

How to Safely Run Claude Code on Ubuntu 24.04

Let’s have a brutal engineering reality check about your AI terminal agents.

If you are switching users locally in your terminal using su, you are destroying your DBus session variables. If you aren't enabling loginctl enable-linger, the Linux kernel is mercilessly killing your rootless Podman containers the exact second you close your SSH client.

And if you haven't set a hard API billing limit? Claude Code is going to read your massive unoptimized repository in a loop and generate an API invoice that will genuinely ruin your week. It is not a flat-fee subscription.

At ServerMO, we just dropped the definitive SRE playbook for running Claude Code securely on Ubuntu bare metal. No SELinux hallucinations in our Quadlet configs—just pristine AppArmor execution, zombie-container prevention, and zero-amnesia headless authentication.

Fix your DevSecOps architecture: 🔗 https://www.servermo.com/howto/install-claude-code-ubuntu-24-04-bare-metal/

[servermo]

Acronis vs JetBackup Which is Best for Dedicated Servers in 2026

Defend against AI generated ransomware in 2026. Compare the budget efficiency of JetBackup with the enterprise bare metal recovery power of

The "Instant Bare-Metal Restore" Lie.

Let’s have a brutal engineering reality check.

Marketing materials for enterprise backup software love promising "instant" bare-metal recoveries. But Site Reliability Engineers know this violates the laws of physics. If your dedicated server dies and you need to pull a 4-Terabyte block-level image from an offsite cloud repository over a standard Gigabit connection... you aren't doing that "instantly." It will take hours.

Calculate your Recovery Time Objective (RTO) based on bandwidth, not brochures.

At ServerMO, we just published a zero-fluff comparison between the two biggest names in the space: Acronis Cyber Protect vs. JetBackup.

Here is what you actually need to know:

The API Key Trap: If your backup agent stores cloud API keys locally on your bare metal server, modern polymorphic ransomware will just authenticate to your remote bucket and wipe your offsite backups too. Cloud storage does not equal safety. You need mathematically immutable storage.

JetBackup (File-Level): It is the undisputed king of shared web hosting (cPanel/Plesk). If a client deletes their WordPress database, they can restore it themselves. But if the whole server burns down? You are manually reinstalling Linux and the control panel before syncing files. Massive downtime.

Acronis (Block-Level): True enterprise reality. It clones the drive sector-by-sector (bootloader, kernel, filesystems) and uses AI kernel heuristics to terminate ransomware. But it eats compute resources and licensing isn't cheap.

Stop relying on the cloud to save you if you haven't implemented the 3-2-1-1-0 framework.

Read the full SRE comparison matrix here: 🔗 Acronis vs JetBackup: The Brutal SRE Infrastructure Review

[servermo]

How to Self Host DeepSeek V4 on Bare Metal GPUs (vLLM SRE Guide)

Escape the AI API tax. Deploy DeepSeek V4 Flash and Pro on NVIDIA H100 and L40S bare metal servers utilizing vLLM expert parallelism and Kon

Stop Paying the API Tax. Self-Host DeepSeek V4 Like a Pro.

We need to have a serious talk about the AI API economy. The one-million-token context window is amazing until you try feeding entire codebases into commercial endpoints and receive a catastrophic monthly invoice.

Processing 50 million tokens a day? You are bleeding money.

The solution is shifting that workload to a ServerMO Bare Metal GPU Server, dropping costs by up to 5x while guaranteeing absolute data sovereignty. But if you’re going to self-host a massive model like DeepSeek V4, you have to stop doing it like an amateur.

Here is the actual Enterprise SRE Blueprint to get it right:

1. The "A100" Illusion A lot of outdated guides tell you to use legacy A100s. Don't. They lack the Hopper Transformer Engine needed for native FP8 acceleration. You need NVIDIA L40S or better.

2. Exact VRAM Arithmetic (No Guesswork) If you are running the FP8 Flash variant, you need exactly 158 GB for weights and 10 GB for a single 1M token KV Cache. Total: 168 GB VRAM. A ServerMO cluster of 4x NVIDIA L40S gives you 192 GB. Warning: That KV cache balloons to 100 GB if 10 users hit it at once. Scale your horizontal infrastructure accordingly.

3. The Storage Bottleneck Stop downloading massive AI models onto local disks or standard NFS. It’s an engineering flaw. You need a Parallel File System (WekaFS) using RDMA to bypass the CPU and stream weights instantly into GPU memory across your cluster.

4. Kong API Security (Kill the "Dirty Key" Hacks) Exposing raw vLLM endpoints to the internet is a disaster waiting to happen. Deploy Kong API Gateway in front of your stack to enforce strict TLS encryption and Enterprise JWT authentication. Because vLLM mimics the OpenAI spec, you can migrate with zero code rewrites.

Stop renting your intelligence from cloud monopolies on volatile spot instances. Reclaim your data. Own the silicon.

The Full Architectural Playbook: 🔗 Self-Host DeepSeek V4 on Bare Metal GPUs

[servermo]

Install Grafana Loki on Ubuntu 24.04 (Enterprise SRE Guide)

Deploy a highly resilient Grafana Loki architecture on Ubuntu 24.04. Master Nginx rate limiting Write Ahead Logs TSDB retention and Grafana

The "Zero-Cost" Monitoring Myth: Architecting Grafana Loki

Let’s address the elephant in the infrastructure room: Zero-cost monitoring is a myth. If you are self-hosting your observability stack, you are still paying for it. Operating a local logging stack incurs an actual Total Cost of Ownership (TCO) encompassing physical SSD wear, network bandwidth, and system administration labor.

The true advantage of self-hosting Grafana Loki isn't that it's magically "free." It’s that it transforms an exponential, unpredictable SaaS bill into a highly predictable infrastructure overhead.

Over on the ServerMO engineering blog, we just dropped a FAANG-level SRE playbook for building an impenetrable, multi-tenant logging stack on Ubuntu 24.04 LTS.

The Blueprint We Cover:

Crash Resilience (WAL): Loki buffers logs in memory. If your server crashes, you lose data. We show you how to enable the Write Ahead Log (WAL) to record operations directly to disk instantaneously.

Bypassing I/O Bottlenecks: Stop scraping flat text files. We utilize Grafana Alloy to connect directly to the systemd journald binary socket for high-fidelity extraction.

The Nginx Gateway: If a rogue application spams logs, your ingestion gateway crashes. We deploy strict Nginx rate limiting (50 requests/sec with burst buffers) to defend the cluster.

Threat Mitigation: Wiring up Fail2ban to instantly block IPs failing your gateway Basic Authentication.

Pair this architecture with ServerMO Dedicated Servers and terminate predatory SaaS billing frameworks permanently.

Dive into the full SRE Playbook here: 🔗 https://www.servermo.com/howto/install-grafana-loki/

[servermo]

Migrating Redis to Valkey on Ubuntu 24.04 (FAANG SRE Runbook)

Transition your enterprise caching layer to Valkey. This SRE architecture guide covers active replication strict TLS enforcement HAProxy cut

Migrating from Redis to Valkey? Read this before you break production.

Migrating enterprise caching layers isn't just about changing a port in your config file and spinning up a new container. It’s an exercise in extreme risk management.

With the recent licensing changes, everyone is rushing to Valkey. But here is the engineering reality: assuming absolute compatibility between Redis 7.2 and Valkey without an audit is a mistake. If your legacy instance relies on proprietary modules like RedisJSON, your replication will fail and Valkey will refuse to ingest the data.

Over on the ServerMO blog, we just dropped a FAANG-level SRE runbook for executing this migration safely on Ubuntu 24.04 LTS.

The Blueprint We Cover:

The Reality of RDB Snapshots: Why you must freeze AOF rewrites and verify your checksums before touching replication pipelines.

Deep TLS Enforcement: Moving beyond basic firewalls. We show you how to disable plaintext, enforce TLSv1.2/1.3, and validate client certificates natively.

The Cutover Pattern: Don't change your application configs and cause a massive cache-miss spike. We use HAProxy to shift traffic seamlessly at the network edge for zero-downtime execution.

Observability: Hooking up Prometheus Valkey exporters to Grafana so you can monitor p99 tail latency in real-time.

Stop relying on basic tutorials. Pair this architecture with ServerMO NVMe Bare Metal to bypass public cloud network jitter and get the raw memory bandwidth your caching layer needs.

Dive into the full SRE Runbook here: 🔗 https://www.servermo.com/howto/migrate-redis-to-valkey/

[servermo]

How to Install CyberPanel on Ubuntu 24.04 LTS

A brutally honest infrastructure architecture guide to deploying CyberPanel on Ubuntu 24.04 LTS covering MySQL tuning S3 backups and Two Fac

The Architecture of Self-Hosting: CyberPanel on Ubuntu 24.04 LTS

If you’re still deploying web panels using "one-click" scripts and default settings, you’re building on sand. In 2026, a production-grade stack requires hardware isolation, database tuning, and aggressive hardening.

We just released a senior-level architecture guide for CyberPanel on Ubuntu 24.04 over at the ServerMO blog. This isn’t a "copy-paste" tutorial—it’s an engineering blueprint.

The Reality Check: Hardware & Latency

Most guides claim you can run a stack on 1GB of RAM. They’re wrong. For a stable environment running OpenLiteSpeed, MariaDB, and PHP-FPM, 4GB is your absolute floor.

Deploying on ServerMO NVMe Bare Metal gives you the dedicated CPU cycles you need to handle heavy WordPress traffic without the "noisy neighbor" jitter of the public cloud.

Inside the Blueprint:

The Database Bottleneck: We show you how to manually tune the InnoDB buffer pool to 60% of your system RAM. Default MySQL configs are the #1 reason panels crash under load.

Zero-Trust Hardening: Moving beyond firewalls. We enforce SSH Key-only authentication and mandatory 2FA for the panel dashboard.

DNS Propagation Logic: Why you should never trigger an SSL issue before your A records are fully propagated.

Disaster Recovery: Automating weekly full-site archives to offsite S3-compatible storage.

Bare metal gives you the performance; our guide gives you the stability.

Read the Full Deep-Dive Tutorial: 🔗 https://www.servermo.com/howto/install-cyberpanel-ubuntu/

[servermo]

10 Best UK Dedicated Server Providers in 2026 (Ranked)

Looking for under 15 milliseconds latency in London? We ranked the best bare metal hosting providers in the United Kingdom based on deep tec

The 2026 UK Infrastructure Audit: Why "London-Only" is a Legacy Mistake 🇬🇧

In 2026, deploying infrastructure in the UK requires more than just picking a brand. With strict UK GDPR laws and a demand for sub-15ms latency, where your iron actually sits matters more than ever.

We’ve conducted a deep technical dive into the Top 10 UK Dedicated Server Providers, scrutinizing everything from network backbones to GPU availability.

The Leaderboard: ServerMO 🏆

ServerMO secures the #1 spot by fundamentally changing how bare metal is delivered in the UK. While most providers crowd into a single London facility, ServerMO operates across 10+ distinct edge locations, including:

📍 Edinburgh, Manchester, Glasgow, Birmingham, Slough, Worcester, Gloucester, and Portsmouth.

The Technical Edge:

Hardware: Direct access to NVIDIA L40S, A100, and RTX A4000 GPUs.

Networking: 10Gbps to 100Gbps unmetered lines with premium BGP routing.

Zero Egress Fees: Unlike the "Cloud Tax" you pay with AWS, ServerMO offers transparent, predictable pricing.

SLA: Aggressive 1-to-4 hour hardware replacement.

How the Competition Compares:

AWS (London): Powerful, but a financial trap for high-bandwidth apps. Those egress fees will swallow your ROI.

Hetzner: Great prices, but zero UK data centers. If your data lives in Germany, you’re losing the latency war and risking GDPR compliance.

OVHcloud: Massive scale, but strictly unmanaged. If you face a hardware fault, you better have a resident SysAdmin ready to troubleshoot alone.

Liquid Web: High-end managed support, but you’ll pay a massive premium for it.

The Verdict

If your business targets British customers or requires proximity to London’s financial hubs, a local bare metal node is mandatory. For the perfect intersection of regional edge hubs, unmetered bandwidth, and elite support, ServerMO is the undisputed engineering champion.

📖 Read the Full Deep-Dive Review here: 🔗 https://www.servermo.com/blogs/best-uk-dedicated-server-providers-2026/

[servermo]

Install and Optimize ClickHouse on Ubuntu 26.04 Bare Metal

Escape public cloud big data fees. Learn the modern 2026 installation for ClickHouse, tiered storage routing, and Vector Search on dedicated

Stop paying the "Data Scan Tax" to public cloud providers.

If you are running ClickHouse on managed cloud services or following outdated Ubuntu guides, you are leaving massive performance on the table and burning your budget.

We just dropped the definitive 2026 ClickHouse Optimization Guide for the brand new Ubuntu 26.04 (Resolute Raccoon). It’s time to treat your database like the high-performance engine it is, not just another container.

Here are the hard engineering truths from the blueprint:

The ZooKeeper Bloat is OVER

ZooKeeper is a heavy Java dependency that eats RAM for breakfast. In 2026, the standard is ClickHouse Keeper a native C++ drop-in replacement. It’s faster, leaner, and won't crash your management nodes.

Bare Metal Tiered Storage > Cloud SSDs

Why pay a premium for cloud storage when you can architect a hybrid setup? We show you how to route active, hot queries to NVMe and automatically move cold data to 18TB HDDs. High-performance analytics at a fraction of the cost.

The "Too Many Parts" Death Loop

Sending thousands of tiny inserts per second? That’s how you crash ClickHouse. You need to enable Async Inserts to buffer queries in RAM before flushing them to disk. We’ve got the exact XML config you need to stop the "Too many parts" error forever.

The AI Vector Search Myth

Marketing gurus say you need a GPU for Vector Search. They’re wrong. ClickHouse Vector Search (HNSW) is heavily CPU-bound, thriving on AVX-512 instructions. Spend your money on high-frequency Bare Metal CPUs, not expensive GPU nodes for your database tier.

Stop using deprecated apt-key commands and legacy architectures. Master the Resolute Raccoon setup.

📖 Read the full Ubuntu 26.04 ClickHouse Guide here: 🔗 https://www.servermo.com/howto/install-clickhouse-ubuntu-26-04/

[servermo]

How to Build a Live Streaming Origin Server with Nginx and NVENC

Stop trusting generic tutorials. Deploy an enterprise grade live streaming origin server using compiled Nginx modules strict CORS security a

Stop trying to build a global Twitch clone on a single server. It’s an engineering lie.

Every generic tutorial out there claims you can handle 10,000 concurrent viewers on a single Nginx node. They are lying. A single server will bottleneck on its network interface limit long before that. What you actually need to build is a High-Performance Origin Server to feed an edge CDN.

If you are building a self-hosted streaming engine using Nginx-RTMP and FFmpeg, here are the brutal realities of 2026:

The GPU Lock Trap

Consumer RTX cards (like the 4090) enforce a strict driver-level lock of around 8 concurrent NVENC sessions. Yes, you can use the open-source nvidia-patch to bypass it, but running uncertified driver hacks in production is a massive compliance risk. For true density, you need Enterprise GPUs (like the L4) on bare metal.

The SSD Killer (RAM Disk Reality)

If you write transient live video segments (.ts chunks) directly to your disk, you will burn through your SSD's read/write endurance in weeks. You must mount a tmpfs RAM disk to handle active live segments.

The Wildcard CORS Disaster

Setting Access-Control-Allow-Origin: * is the absolute dumbest thing you can do. You are literally begging random websites to embed your player and steal your expensive unmetered bandwidth. Hardcode your exact domains.

The LL-HLS Latency Lie

"Low Latency HLS" is a marketing term. Even heavily tuned to 1-second chunks, expect a 4 to 8-second delay. If your platform demands true sub-second, real-time interaction, you have to abandon Nginx-RTMP entirely and build a WebRTC pipeline.

Stop relying on default apt packages. Compile from source, optimize your filter_complex pipelines, and master your ingest architecture.

📖 Read our brutally honest Nginx-RTMP Origin Server Blueprint here: 🔗 https://www.servermo.com/howto/build-live-streaming-server-nginx-rtmp/

[servermo]

How to Migrate MySQL to ClickHouse with Zero Downtime

Stop using deprecated engines. Master the modern 2026 CDC pipeline using Debezium and Redpanda to sync MySQL with ClickHouse on dedicated ha

MaterializedMySQL is officially DEAD. Stop following outdated database tutorials.

If you are trying to offload heavy analytical queries from MySQL to ClickHouse, almost every old guide will tell you to use the MaterializedMySQL engine. Don't do it. It was highly experimental, fundamentally flawed, and the ClickHouse team officially removed it in version 24.12.

If you want a true Zero-Downtime migration, here are the brutal engineering realities of 2026:

The SaaS Egress Trap

Managed CDC tools are convenient, but syncing terabytes of operational data across cloud regions will bankrupt you with network egress fees. Running Redpanda (a lightweight C++ Kafka alternative) on Unmetered Bare Metal is the only way to get sub-millisecond replication without the billing shock.

The FINAL Keyword CPU Death

Many blogs tell you to use SELECT ... FINAL to fetch updated rows in ClickHouse. Do not do this. It causes massive CPU spikes. The enterprise way is to use the argMax() function to fetch the latest state without locking your entire database.

Tombstones & The Storage Tax

ClickHouse is append-only. When a row is deleted in MySQL, Debezium sends a tombstone record. You have to route this to a ReplacingMergeTree table and schedule an OPTIMIZE command during off-peak hours to force a cleanup, or your storage will amplify endlessly.

Stop crashing your analytics nodes with bad schemas. Master the 2026 Change Data Capture (CDC) pipeline.

[Action Required: Paste your actual link below before publishing]

📖 Read our full MySQL to ClickHouse Migration Blueprint here: 🔗 How to Migrate MySQL to ClickHouse with Zero Downtime

[servermo]

The Agentic Execution Loop: Distributed Systems & AI Inference

Beyond single-node optimization: The engineering realities of scaling autonomous agents, API Network Colocation, sequential tool calling, an

Everyone is obsessing over GPU VRAM, but scaling AI agents is actually a distributed systems nightmare.

The AI industry is obsessed with single-node optimization—NVLink, PCIe lanes, and teraflops. But if you are actually building autonomous AI agents in 2026, you know the brutal truth: scaling AI isn't a GPU problem anymore, it’s a network latency problem.

Here are the harsh realities of agentic execution loops:

The N+1 Tool Calling Death Trap

An AI agent doesn't just generate text; it runs in a loop (Think → Query DB → Call API → Evaluate). If your agent needs the result of API Call A to make API Call B, and your network latency is 80ms, you just introduced hundreds of milliseconds of dead time. Your $30,000 GPU is literally sitting idle, waiting on the speed of light.

You Can't Beat the Physics of API Proximity

If your inference nodes are geographically far from the enterprise APIs your agents are calling, your recursive loop is dead on arrival. You have to physically colocate your bare metal nodes in data center epicenters like Ashburn, VA to drop that Round Trip Time (RTT) down to 1-5ms.

The OpenTelemetry "Log Tax"

When a multi-agent system slows down, you need distributed tracing. But if you do this on a public cloud, exporting terabytes of trace data will incur massive egress fees. True scaling requires unmetered bare metal where you can run heavy O11y stacks and raw kernel eBPF tracing without inflating your monthly OpEx.

Stop benchmarking single GPUs and start architecting for the network.

[servermo]

IPTV Engineering 2026: The Brutal Realities of Scaling

Stop relying on marketing myths. Learn how to handle GPU deadlocks, stream token leakage, and true stateful active-active failover in IPTV a

Launching 30 live streams at once will deadlock your GPU. Stop treating production like a lab experiment. 🛑

Most online IPTV tutorials are marketing fluff. They don't tell you the brutal engineering realities of scaling a streaming service. If you are building a high-load IPTV architecture, here are the hard truths you need to accept:

The Stress Test Death Trap ⚡

If you use a command like xargs to launch 30 FFmpeg streams simultaneously, you will cause an immediate initialization spike, flood your PCIe bus, and trigger a VRAM allocation deadlock. You must write a Staggered Startup script that introduces sleep delays to gently load the encoder.

Kubernetes is Not Enough for Live TV 🔄

Relying on K8s to auto-restart a crashed FFmpeg pod is unacceptable. A cold pod startup takes 5-10 seconds, causing a massive stream blackout for your viewers. You need Stateful Active-Active Redundancy across two entirely separate bare metal nodes so the CDN edge can failover instantly.

Token Leakage will Drain Your Bandwidth 🔒

Implementing standard JWTs is useless if a user just copies their valid token and posts it on Reddit. Thousands of unauthorized users will piggyback on that stream. You must enforce strict IP Binding inside the JWT payload at the CDN edge.

The Cloud Egress Tax will Bankrupt You 💸

Pushing 5,000 concurrent viewers on a 4Mbps stream requires ~20 Gbps of continuous traffic. If you do this on AWS or GCP, the per-GB egress fees will instantly bankrupt your business. This is why true IPTV systems rely solely on Unmetered Bare Metal Servers.

Stop relying on marketing myths. Master your streaming infrastructure.

[servermo]

Install & Tune PostgreSQL on Ubuntu 24.04 Bare Metal

Escape the 128MB memory trap. Learn modern PostgreSQL 16/17 installation, NVMe WAL separation, and disaster recovery on Ubuntu 24.04.

Your 128GB Bare Metal server is running PostgreSQL like a Raspberry Pi. 💀🐘

Most tutorials online teach you how to install PostgreSQL, but they leave out a brutal engineering reality: If you just run apt install postgresql on Ubuntu 24.04, your massive database defaults to using a measly 128MB of RAM for its cache.

Here are the harsh truths about tuning PostgreSQL for modern infrastructure:

The "25% RAM" Myth is Dead 🛑

You will often read that you should set shared_buffers to 25% of your total RAM. On a 16GB server? Sure. On a 256GB Bare Metal server? Allocating 64GB to shared_buffers is a massive mistake that causes inefficient "double-buffering". Modern DBAs cap this at 16GB-32GB and let the Linux Kernel Page Cache do the heavy lifting.

The OOM-Killer Trap 💥

If you set work_mem too high (e.g., 256MB) and allow 1,000 direct connections, you will instantly consume 256GB of RAM. The Linux Out-Of-Memory (OOM) killer will wake up and violently terminate your database. You absolutely must use a connection pooler like PgBouncer.

NVMe WAL Separation ⚡

Stop using HDD-era RAID 10 logic. The true architectural secret to database speed on enterprise NVMe SSDs is physically separating your Write-Ahead Log (WAL). Put your main DB on one drive, and point your WAL to a completely separate, dedicated NVMe drive to eliminate disk contention.

Stop relying on default configs and managed-cloud marketing. Master your bare metal.