1. Technology Is an Infinite Game and That Is the Point
Technology has no finish line. There is no end state, no final architecture, no moment where you can stand back and declare victory and go home. It is an infinite game made up of a long sequence of hard fought battles, each one draining, each one expensive, each one slower than anyone would like. The moment you solve one problem, the context shifts and the solution becomes the next constraint.
Everything feels too expensive, too difficult and too slow. Under that pressure, a familiar thought pattern emerges. If only we could transfer the risk to someone else. If only we could write enough SLAs, sharpen enough penalties and load the contract until gravity itself guarantees success. If only we could hire lawyers, run a massive outsourcing RFP and make the uncertainty go away.
This is the first lie of the infinite game. Risk does not disappear just because you have moved it onto someone else’s balance sheet. It simply comes back later with interest, usually at the worst possible time.
2. The Euphoria Phase and the Hangover That Follows
The outsourcing cycle always begins with euphoria. There is a media statement. Words like strategic and synergies are deployed liberally. Executive decks are filled with arrows pointing up and to the right. Contracts are signed. Photos are taken. Everyone congratulates each other on having made the hard decisions. Then reality arrives quietly.
Knowledge transfer begins and immediately reveals that much of what matters was never written down. Attrition starts to bite, first at the edges, then at the core. The people who actually understood why things were built the way they were begin to leave, often because they were treated as interchangeable delivery units rather than as the source of the IP itself.
You attempted to turn IP creation into the procurement of pencils. You specified outputs, measured compliance and assumed the essence of the work could be reduced to a checklist. What you actually outsourced was your ability to adapt.
3. Finite Contracts Versus Infinite Competition
Outsourcing is fundamentally a finite game. It is about grinding every last cent out of a well defined specification. It is about predictability, cost control and contractual certainty. Those are not bad things in isolation. Competition, however, is infinite.
You are not playing to complete a statement of work. You are playing on a chessboard with an infinite number of possible moves, where the only goal is to win. To be better than the competition. To innovate faster than they do. To thrive in an environment that changes daily.
The absurdity of this mismatch is often visible in the contracts themselves. Somewhere deep in the appendices you will find a line item for innovation spend, because the board asked for it. As if innovation can be pre purchased, time boxed and invoiced monthly. Innovation is not a deliverable. It is an outcome of ownership, proximity and deep understanding.
4. When Outsourcing Actually Works
Outsourcing does work, but only under very specific conditions. It works when the thing you are outsourcing is not mission critical. When it is a side show. When failure is survivable and learning is optional.
Payroll systems, commodity infrastructure, clearly bounded operational tasks can often be externalised safely. The moment the outsourced capability becomes core to differentiation, speed or revenue generation, the model starts to collapse under its own weight.
The more central the capability is to winning the infinite game, the more dangerous it is to distance yourself from it.
5. Frameworks as a Way Down the Complexity Food Chain
There is a more subtle version of the same instinct, and it shows up in the overuse of vendor frameworks. Platforms like Salesforce allow you to express your IP in a controlled and well defined way. You are deliberately moving yourself down the complexity food chain. Things become easier to reason about, easier to hire for and easier to operate.
There is nothing inherently wrong with this. In many cases it is a rational trade off.
The danger appears when this pattern is applied indiscriminately. When every problem is forced into a vendor shaped abstraction. When flexibility is traded for integration points until you find yourself wrapped in a beautifully integrated set of shackles. Each individual decision looked sensible. The aggregate outcome is paralysis.
You did not remove complexity. You just externalised it and made it harder to escape.
6. No Strategic End State Exists
Technology strategy does not converge. There is no strategic end state. Looking for one is like looking for a strategic newspaper. It changes every day.
Mortgaging today to pay for a hypothetical tomorrow is how organisations lose their ability to move. Long term plans that require years of faith before any value is delivered are a luxury few businesses can afford. Try to operate with a tiny balance sheet. Keep commitments short. Keep feedback loops tight.
Whatever you do, do it efficiently and quickly. Get it to production. Make money from it. Learning that does not touch reality is just theory.
Anyone who walks into the room with a five year roadmap before delivering anything meaningful should be sent back out of it. Some things genuinely do take time, but sustainable businesses run on a varied delivery diet. Small wins, medium bets and the occasional long horizon investment, all running in parallel.
7. The Cost of Playing the Infinite Game
Technology is hard. It always has been. It demands stamina, humility and a tolerance for discomfort. There are no permanent victories, only temporary advantages. The frustration you feel is not a sign of failure. It is the admission price for playing an infinite game.
You do not win by outsourcing the game itself. You win by staying close to the work, owning the risk and being willing to fight the next battle with clear eyes and minimal baggage.
8. The Question Nobody Wants to Ask About Outsourcing
There is a question that almost never gets asked out loud, despite how obvious it is once you say it. How many companies that specialise in outsourcing actually lose money? The answer is vanishingly few. They are very good at what they do. Much better than you.
They have better lawyers than you. They have refined their contracts, their commercial models and their delivery mechanics over decades. They will kill you softly with their way of working. With planning artefacts that look impressive but slow everything down. With invoicing structures that extract value for every ambiguity. With change requests for things you implicitly assumed were included, but were never explicitly written down.
They do not know what your business truly wants or needs, and more importantly, they do not care. They are not misaligned out of malice, they are misaligned by design. Their incentives are not your incentives. Their goal is not to win your market, delight your customers or protect your brand. Their goal is to run a profitable outsourcing business.
They will absolutely cut your costs, at least initially. Headcount numbers go down. Unit costs look better. The spreadsheet tells a comforting story and everyone relaxes. For a while, it feels like the right decision.
Then, slowly, the cracks begin to appear.
Velocity drops. Small changes become negotiations. Workarounds replace understanding. You realise that decisions are being optimised for contractual safety rather than business outcomes. Innovation dries up, except for what can be safely branded as innovation without threatening the delivery model.
By the time this becomes visible to leadership, you are already in trouble. You have missed out on years of engagement, learning and organic innovation. The people who cared deeply about your systems and customers are gone. What remains is a brittle, over constrained estate that nobody fully understands, including the people being paid to run it.
You now have a basket case to fix, under pressure, with fewer options than before. The short term cost savings have been repaid many times over in lost opportunity, lost capability and lost time. This is not a failure of execution. It is the predictable outcome of trying to play an infinite game using finite tools.
9. Conclusion: Embrace the Reality of the Infinite Game
In the end, the frustration we feel with technology, the slowness, cost, complexity, risk, and the urge to outsource it all, is a symptom of a deeper mismatch between the type of game we are playing and the mindset we bring to it. In business and technology there is no final victory, no stable “end-state” where we can declare ourselves done and walk away. We are participating in an infinite game — one where the objective isn’t to “win once” but to remain in the game, continuously adapting, learning, and advancing.
Finite tools like contracts, SLAs, RFPs, tightly bounded outsourcing arrangements, are not evil in themselves. They work well inside a finite context where boundaries, rules and outcomes are clear. But when we try to impose them onto something that has no finish line, we almost guarantee friction, misalignment, and eventual stagnation. The irony is that in trying to control uncertainty, we inadvertently kill the very flexibility and innovation that keep us relevant.
The real lesson is not to avoid complexity, but to embrace the infinite nature of what we’re doing. Technology isn’t a project you finish, it’s a landscape you navigate. Outsourcing may shift certain operational burdens, but it doesn’t transfer the necessity to learn, to iterate, to confront ambiguity, or to sustain competitive advantage. Those remain firmly in your hands.
So if there is a single strategic takeaway, it is this:
Stop looking for an end. Start building a rhythm. Deliver value early. Learn fast. And keep forging ahead.
That is how you thrive not just in technology, but in any domain where the game has no end.
The in memory data store landscape fractured in March 2024 when Redis Inc abandoned its BSD 3-clause licence in favour of the dual RSALv2/SSPLv1 model. The community response was swift and surgical: Valkey emerged as a Linux Foundation backed fork, supported by AWS, Google Cloud, Oracle, Alibaba, Tencent, and Ericsson. Eighteen months later, both projects have diverged significantly, and the choice between them involves far more than licensing philosophy.
1. The Fork That Changed Everything
When Redis Inc made its licensing move, the stated rationale was protecting against cloud providers offering Redis as a managed service without contribution. The irony was immediate. AWS and Google Cloud responded by backing Valkey with their best Redis engineers. Tencent’s Binbin Zhu alone had contributed nearly a quarter of all Redis open source commits. The technical leadership committee now has over 26 years of combined Redis experience and more than 1,000 commits to the codebase.
Redis Inc CEO Rowan Trollope dismissed the fork at the time, asserting that innovation would differentiate Redis. What he perhaps underestimated was that the innovators had just walked out the door.
By May 2025, Redis pivoted again, adding AGPLv3 as a licensing option for Redis 8. The company brought back Salvatore Sanfilippo (antirez), Redis’s original creator. The messaging was careful: “Redis as open source again.” But the damage to community trust was done. As Kyle Davis, a Valkey maintainer, stated after the September 2024 Valkey 8.0 release: “From this point forward, Redis and Valkey are two different pieces of software.”
2. Architecture: Same Foundation, Diverging Paths
Both Redis and Valkey maintain the single threaded command execution model that ensures atomicity without locks or context switches. This architectural principle remains sacred. What differs is how each project leverages additional threads for I/O operations.
2.1 Threading Models
Redis introduced multi threaded I/O in version 6.0, offloading socket reads and writes to worker threads while keeping data manipulation single threaded. Redis 8.0 enhanced this with a new I/O threading model claiming 112% throughput improvement when setting io-threads to 8 on multi core Intel CPUs.
Valkey took a more aggressive approach. The async I/O threading implementation in Valkey 8.0, contributed primarily by AWS engineers, allows the main thread and I/O threads to operate concurrently. The I/O threads handle reading, parsing, writing responses, polling for I/O events, and even memory deallocation. The main thread orchestrates jobs to I/O threads while executing commands, and the number of active I/O threads adjusts dynamically based on load.
The results speak clearly. On AWS Graviton r7g instances, Valkey 8.0 achieves 1.2 million queries per second compared to 380K QPS in Valkey 7.2 (a 230% improvement). Independent benchmarks from Momento on c8g.2xlarge instances (8 vCPU) showed Valkey 8.1.1 reaching 999.8K RPS on SET operations with 0.8ms p99 latency, while Redis 8.0 achieved 729.4K RPS with 0.99ms p99 latency. That is 37% higher throughput on writes and 60%+ faster p99 latencies on reads.
2.2 Memory Efficiency
Valkey 8.0 introduced a redesigned hash table implementation that reduces memory overhead per key. The 8.1 release pushed further, observing roughly 20 byte reduction per key-value pair for keys without TTL, and up to 30 bytes for keys with TTL. For a dataset with 50 million keys, that translates to roughly 1GB of saved memory.
Redis 8.0 counters with its own optimisations, but the Valkey improvements came from engineers intimately familiar with the original Redis codebase. Google Cloud’s benchmarks show Memorystore for Valkey 8.0 achieving 2x QPS at microsecond latency compared to Memorystore for Redis Cluster.
3. Feature Comparison
3.1 Core Data Types
Both support the standard Redis data types: strings, hashes, lists, sets, sorted sets, streams, HyperLogLog, bitmaps, and geospatial indexes. Both support Lua scripting and Pub/Sub messaging.
3.2 JSON Support
Redis 8.0 bundles RedisJSON (previously a separate module) directly into core, available under the AGPLv3 licence. This provides native JSON document storage with partial updates and JSONPath queries.
Valkey responded with valkey-json, an official module compatible with Valkey 8.0 and above. As of Valkey 8.1, JSON support is production-ready through the valkey bundle container that packages valkey-json 1.0, valkey-bloom 1.0, valkey-search 1.0, and valkey-ldap 1.0 together.
3.3 Vector Search
This is where the AI workload story becomes interesting.
Redis 8.0 introduced vector sets as a new native data type, designed by Sanfilippo himself. It provides high dimensional similarity search directly in core, positioning Redis for semantic search, RAG pipelines, and recommendation systems.
Valkey’s approach is modular. valkey-search provides KNN and HNSW approximate nearest neighbour algorithms, capable of searching billions of vectors with millisecond latencies and over 99% recall. Google Cloud contributed their vector search module to the project, and it is now the official search module for Valkey OSS. Memorystore for Valkey can perform vector search at single digit millisecond latency on over a billion vectors.
The architectural difference matters. Redis embeds vector capability in core; Valkey keeps it modular. For organisations wanting a smaller attack surface or not needing vector search, Valkey’s approach offers more control.
3.4 Probabilistic Data Structures
Both now offer Bloom filters. Redis bundles RedisBloom in core; Valkey provides valkey-bloom as a module. Bloom filters use roughly 98% less memory than traditional sets for membership testing with an acceptable false positive rate.
3.5 Time Series
Redis 8.0 bundles RedisTimeSeries in core. Valkey does not yet have a native time series module, though the roadmap indicates interest.
3.6 Search and Query
Redis 8.0 includes the Redis Query Engine (formerly RediSearch), providing secondary indexing, full-text search, and aggregation capabilities.
Valkey search currently focuses on vector search but explicitly states its goal is to extend Valkey into a full search engine supporting full-text search. This is roadmap, not shipped product, as of late 2025.
4. Licensing: The Uncomfortable Conversation
4.1 Redis Licensing (as of Redis 8.0)
Redis now offers a tri licence model: RSALv2, SSPLv1, and AGPLv3. Users choose one.
AGPLv3 is OSI approved open source, but organisations often avoid it due to its copyleft requirements. If you modify Redis and offer it as a network service, you must release your modifications. Many enterprise legal teams treat AGPL as functionally similar to proprietary for internal use policies.
RSALv2 and SSPLv1 are source available but not open source by OSI definition. Both restrict offering Redis as a managed service without licensing arrangements.
The practical implication: most enterprises consuming Redis 8.0 will either use it unmodified (which sidesteps AGPL concerns) or license Redis commercially.
4.2 Valkey Licensing
Valkey remains BSD 3-clause. Full stop. You can fork it, modify it, commercialise it, and offer it as a managed service without restriction. This is why AWS, Google Cloud, Oracle, and dozens of others are building their managed offerings on Valkey.
For financial services institutions subject to regulatory scrutiny around software licensing, Valkey’s licence clarity is a non-trivial advantage.
AWS has made its position clear through pricing. The discounts for Valkey versus Redis OSS are substantial and consistent across services.
5.1 Annual Cost Comparison by Cluster Size
The following table shows annual costs for typical ElastiCache node-based deployments in us-east-1 using r7g Graviton3 instances. All configurations assume high availability with one replica per shard. Pricing reflects on-demand rates; reserved instances reduce costs further but maintain the same 20% Valkey discount.
For serverless deployments, the 33% discount on both storage and compute makes the differential even more pronounced at scale.
Workload
Storage
Requests/sec
Redis OSS/year
Valkey/year
Savings
Small
5GB
10,000
$5,475
$3,650
$1,825
Medium
25GB
50,000
$16,350
$10,900
$5,450
Large
100GB
200,000
$54,400
$36,250
$18,150
XL
500GB
1,000,000
$233,600
$155,700
$77,900
Note: Serverless calculations assume simple GET/SET operations (1 ECPU per request) with sub-1KB payloads. Complex operations on sorted sets or hashes consume proportionally more ECPUs.
5.2 Memory Efficiency Multiplier
The above comparisons assume identical node sizing, but Valkey 8.1’s memory efficiency improvements often allow downsizing. AWS documented a real customer case where upgrading from ElastiCache for Redis OSS to Valkey 8.1 reduced memory usage by 36%, enabling a downgrade from r7g.xlarge to r7g.large nodes. Combined with the 20% engine discount, total savings reached 50%.
For the Large Cluster example above, if memory efficiency allows downsizing from r7g.2xlarge to r7g.xlarge:
Scenario
Configuration
Annual Cost
vs Redis OSS Baseline
Redis OSS (baseline)
12× r7g.2xlarge
$91,764
—
Valkey (same nodes)
12× r7g.2xlarge
$73,380
-20%
Valkey (downsized)
12× r7g.xlarge
$36,792
-60%
This 60% saving reflects real-world outcomes when combining engine pricing with memory efficiency gains.
5.3 ElastiCache Serverless
ElastiCache Serverless charges for data storage (GB-hours) and compute (ElastiCache Processing Units or ECPUs). One ECPU covers 1KB of data transferred for simple GET/SET operations. More complex commands like HMGET consume ECPUs proportional to vCPU time or data transferred, whichever is higher.
In us-east-1, ElastiCache Serverless for Valkey prices at $0.0837/GB-hour for storage and $0.00227/million ECPUs. ElastiCache Serverless for Redis OSS prices at $0.125/GB-hour for storage and $0.0034/million ECPUs. That is 33% lower on storage and 33% lower on compute for Valkey.
The minimum metered storage is 100MB for Valkey versus 1GB for Redis OSS. This enables Valkey caches starting at $6/month compared to roughly $91/month for Redis OSS.
For a reference workload of 10GB average storage and 50,000 requests/second (simple GET/SET, sub-1KB payloads), the monthly cost breaks down as follows. Storage runs 10 GB × $0.0837/GB-hour × 730 hours = $611/month for Valkey versus $912.50/month for Redis OSS. Compute runs 180 million ECPUs/hour × 730 hours × $0.00227/million = $298/month for Valkey versus $446/month for Redis OSS. Total monthly cost is roughly $909 for Valkey versus $1,358 for Redis OSS, a 33% saving.
5.4 ElastiCache Node Base
For self managed clusters where you choose instance types, Valkey is priced 20% lower than Redis OSS across all node types.
A cache.r7g.xlarge node (4 vCPU, 26.32 GiB memory) in us-east-1 costs $0.449/hour for Valkey versus $0.561/hour for Redis OSS. Over a month, that is $328 versus $410 per node. For a cluster with 6 nodes (3 shards, 1 replica each), annual savings reach $5,904.
Reserved nodes offer additional discounts (up to 55% for 3 year all upfront) on top of the Valkey pricing advantage. Critically, if you hold Redis OSS reserved node contracts and migrate to Valkey, your reservations continue to apply. You simply receive 20% more value from them.
5.5 MemoryDB
Amazon MemoryDB, the durable in-memory database with multi AZ persistence, follows the same pattern. MemoryDB for Valkey is 30% lower on instance hours than MemoryDB for Redis OSS.
A db.r6g.xlarge node in us-west-2 costs $0.432/hour for Valkey versus approximately $0.617/hour for Redis OSS. For a typical HA deployment (1 shard, 1 primary, 1 replica), monthly costs run $631 for Valkey versus $901 for Redis OSS.
MemoryDB for Valkey also eliminates data written charges up to 10TB/month. Above that threshold, pricing is $0.04/GB, which is 80% lower than MemoryDB for Redis OSS.
5.6 Data Tiering Economics
For workloads with cold data that must remain accessible, ElastiCache and MemoryDB both support data tiering on r6gd node types. This moves infrequently accessed data from memory to SSD automatically.
A db.r6gd.4xlarge with data tiering can store 840GB total (approximately 105GB in memory, 735GB on SSD) at significantly lower cost than pure in-memory equivalents. For compliance workloads requiring 12 months of data retention, this can reduce costs by 52.5% compared to fully in memory configurations while maintaining low millisecond latencies for hot data.
5.7 Scaling Economics
ElastiCache Serverless for Valkey 8.0 scales dramatically faster than 7.2. In AWS benchmarks, scaling from 0 to 5 million RPS takes under 13 minutes on Valkey 8.0 versus 50 minutes on Valkey 7.2. The system doubles supported RPS every 2 minutes versus every 10 minutes.
For burst workloads, this faster scaling means lower peak latencies. The p99 latency during aggressive scaling stays under 8ms for Valkey 8.0 versus potentially spiking during the longer scaling windows of earlier versions.
5.8 Migration Economics
AWS provides zero-downtime, in place upgrades from ElastiCache for Redis OSS to ElastiCache for Valkey. The process is a few clicks in the console or a single CLI command:
Your reserved node pricing carries over, and you immediately begin receiving the 20% discount on node based clusters or 33% discount on serverless. There is no migration cost beyond the time to validate application compatibility.
5.9 Total Cost of Ownership
For an enterprise running 100GB across 10 ElastiCache clusters with typical caching workloads, the annual savings from Redis OSS to Valkey are substantial:
Serverless scenario (10 clusters, 10GB each, 100K RPS average per cluster): roughly $109,000/year on Valkey versus $163,000/year on Redis OSS, saving $54,000 annually.
Node-based scenario (10 clusters, cache.r7g.2xlarge, 3 shards + 1 replica each): roughly $315,000/year on Valkey versus $394,000/year on Redis OSS, saving $79,000 annually.
These numbers exclude operational savings from faster scaling, lower latencies reducing retry logic, and memory efficiency improvements allowing smaller node selections.
6. Google Cloud and Azure Considerations
Google Cloud Memorystore for Valkey is generally available with a 99.99% SLA. Committed use discounts offer 20% off for one-year terms and 40% off for three-year terms, fungible across Memorystore for Valkey, Redis Cluster, Redis, and Memcached. Google was first to market with Valkey 8.0 as a managed service.
Azure offers Azure Cache for Redis as a managed service, based on licensed Redis rather than Valkey. Microsoft’s agreement with Redis Inc means Azure customers do not currently have a Valkey option through native Azure services. For Azure-primary organisations wanting Valkey, options include self-managed deployments on AKS or multi-cloud architectures leveraging AWS or GCP for caching.
Redis Cloud (Redis Inc’s managed offering) operates across AWS, GCP, and Azure with consistent pricing. Commercial quotes are required for production workloads, making direct comparison difficult, but the pricing does not include the aggressive discounting that cloud providers apply to Valkey.
7. Third Party Options
Upstash offers a true pay-per-request serverless Redis-compatible service at $0.20 per 100K requests plus $0.25/GB storage. For low-traffic applications (under 1 million requests/month with 1GB storage), Upstash costs roughly $2.25/month versus $91+/month for ElastiCache Serverless Redis OSS. Upstash also provides a REST API for environments where TCP is restricted, such as Cloudflare Workers.
Dragonfly, KeyDB, and other Redis-compatible alternatives exist but lack the cloud provider backing and scale validation that Valkey has demonstrated.
Enable with io-threads N in configuration. Start with core count minus 2 for the I/O thread count. The system dynamically adjusts active threads based on load, so slight overprovisioning is safe.
For TLS workloads, Valkey 8.1 offloads TLS negotiation to I/O threads, improving new connection rates by roughly 300%.
9.2 Memory Defragmentation
Valkey 8.1 reduced active defrag cycle time to 500 microseconds with anti-starvation protection. This eliminates the historical issue of 1ms+ latency spikes during defragmentation.
9.3 Cluster Scaling
Valkey 8.0 introduced automatic failover for empty shards and replicated migration states. During slot movement, cluster consistency is maintained even through node failures. This was contributed by Google and addresses real production pain from earlier Redis cluster implementations.
10. The Verdict
The Redis fork has produced genuine competition for the first time in the in-memory data store space. Valkey is not merely a “keep the lights on” maintenance fork. It is evolving faster than Redis in core performance characteristics, backed by engineers who wrote much of the original Redis codebase, and supported by the largest cloud providers.
For enterprise architects, the calculus is increasingly straightforward. Unless you have specific dependencies on Redis 8’s bundled modules (particularly time series or full-text search), Valkey offers superior performance, clearer licensing, and lower costs on managed platforms.
The commercial signals are unambiguous. AWS prices Valkey 20-33% below Redis OSS on ElastiCache and 30% below on MemoryDB. Reserved node contracts transfer seamlessly. Migration is zero-downtime. The incentive structure points one direction.
The Redis licence changes in 2024 were intended to monetise cloud provider usage. Instead, they unified the cloud providers behind an alternative that is now outperforming the original. The return to AGPLv3 in 2025 acknowledges the strategic error, but the community momentum has shifted.
Redis is open source again. But the community that made it great is building Valkey.
Running WordPress on ARM-based Graviton instances delivers up to 40% better price-performance compared to x86 equivalents. This guide provides production-ready scripts to deploy an optimised WordPress stack in minutes, plus everything you need to migrate your existing site.
Why Graviton for WordPress?
Graviton3 processors deliver:
40% better price-performance vs comparable x86 instances
Up to 25% lower cost for equivalent workloads
60% less energy consumption per compute hour
Native ARM64 optimisations for PHP 8.x and MariaDB
The t4g.small instance (2 vCPU, 2GB RAM) at ~$12/month handles most WordPress sites comfortably. For higher traffic, t4g.medium or c7g instances scale beautifully.
# 1. Launch instance (local machine)
./launch-graviton-wp.sh
# 2. SSH in and setup WordPress
ssh -i ~/.ssh/key.pem ec2-user@IP
sudo ./setup-wordpress.sh
# 3. If migrating - on old server
./wp-export.sh
scp /tmp/wp-migration/wordpress-migration-*.tar.gz ec2-user@NEW_IP:/tmp/
# 4. If migrating - on new server
sudo ./wp-import.sh /tmp/wordpress-migration-*.tar.gz
This setup delivers a production-ready WordPress installation that’ll handle significant traffic while keeping your AWS bill minimal. The combination of Graviton’s price-performance, Caddy’s efficiency, and properly-tuned PHP creates a stack that punches well above its weight class.
Java 25 introduces a significant enhancement to application startup performance through the AOT (Ahead of Time) cache feature, part of JEP 483. This capability allows the JVM to cache the results of class loading, bytecode parsing, verification, and method compilation, dramatically reducing startup times for subsequent application runs. For enterprise applications, particularly those built with frameworks like Spring, this represents a fundamental shift in how we approach deployment and scaling strategies.
2. Understanding Ahead of Time Compilation
2.1 What is AOT Compilation?
Ahead of Time compilation differs from traditional Just in Time (JIT) compilation in a fundamental way: the compilation work happens before the application runs, rather than during runtime. In the standard JVM model, bytecode is interpreted initially, and the JIT compiler identifies hot paths to compile into native machine code. This process consumes CPU cycles and memory during application startup and warmup.
AOT compilation moves this work earlier in the lifecycle. The JVM can analyze class files, perform verification, parse bytecode structures, and even compile frequently executed methods to native code ahead of time. The results are stored in a cache that subsequent JVM instances can load directly, bypassing the expensive initialization phase.
2.2 The AOT Cache Architecture
The Java 25 AOT cache operates at multiple levels:
Class Data Sharing (CDS): The foundation layer that shares common class metadata across JVM instances. CDS has existed since Java 5 but has been significantly enhanced.
Application Class Data Sharing (AppCDS): Extends CDS to include application classes, not just JDK classes. This reduces class loading overhead for your specific application code.
Dynamic CDS Archives: Automatically generates CDS archives based on the classes loaded during a training run. This is the key enabler for the AOT cache feature.
Compiled Code Cache: Stores native code generated by the JIT compiler during training runs, allowing subsequent instances to load pre-compiled methods directly.
The cache is stored as a memory mapped file that the JVM can load efficiently at startup. The file format is optimized for fast access and includes metadata about the Java version, configuration, and class file checksums to ensure compatibility.
2.3 The Training Process
Training is the process of running your application under representative load to identify which classes to load, which methods to compile, and what optimization decisions to make. During training, the JVM records:
All classes loaded and their initialization order
Method compilation decisions and optimization levels
Inline caching data structures
Class hierarchy analysis results
Branch prediction statistics
Allocation profiles
The training run produces an AOT cache file that captures this runtime behavior. Subsequent JVM instances can then load this cache and immediately benefit from the pre-computed optimization decisions.
3. GraalVM Native Image vs Java 25 AOT Cache
3.1 Architectural Differences
GraalVM Native Image and Java 25 AOT cache solve similar problems but use fundamentally different approaches.
GraalVM Native Image performs closed world analysis at build time. It analyzes your entire application and all dependencies, determines which code paths are reachable, and compiles everything into a single native executable. The result is a standalone binary that:
Starts in milliseconds (typically 10-50ms)
Uses minimal memory (often 10-50MB at startup)
Contains no JVM or bytecode interpreter
Cannot load classes dynamically without explicit configuration
Requires build time configuration for reflection, JNI, and resources
Java 25 AOT Cache operates within the standard JVM runtime. It accelerates the JVM startup process but maintains full Java semantics:
Starts faster than standard JVM (typically 2-5x improvement)
Retains full dynamic capabilities (reflection, dynamic proxies, etc.)
Works with existing applications without code changes
Supports dynamic class loading
Falls back to standard JIT compilation for uncached methods
3.2 Performance Comparison
For a typical Spring Boot application (approximately 200 classes, moderate dependency graph):
Standard JVM: 8-12 seconds to first request Java 25 AOT Cache: 2-4 seconds to first request GraalVM Native Image: 50-200ms to first request
Real world measurements from a medium complexity Spring Boot application (e-commerce platform with 200+ beans):
Cold Start (no AOT cache):
Application startup time: 11.3s
Memory at startup: 285MB RSS
Time to first request: 12.1s
Peak memory during warmup: 420MB
With AOT Cache (trained):
Application startup time: 2.8s (75% improvement)
Memory at startup: 245MB RSS (14% improvement)
Time to first request: 3.2s (74% improvement)
Peak memory during warmup: 380MB (10% improvement)
Savings Breakdown:
Eliminated 8.5s of initialization overhead
Saved 40MB of temporary objects during startup
Reduced GC pressure during warmup by ~35%
First meaningful response 8.9s faster
For a 10 instance deployment, this translates to:
85 seconds less total startup time per rolling deployment
Faster autoscaling response (new pods ready in 3s vs 12s)
Reduced CPU consumption during startup phase by ~60%
5.4 Spring Boot Actuator Integration
Monitor AOT cache effectiveness via custom metrics:
@Component
public class AotCacheMetrics {
private final MeterRegistry registry;
public AotCacheMetrics(MeterRegistry registry) {
this.registry = registry;
exposeAotMetrics();
}
private void exposeAotMetrics() {
Gauge.builder("aot.cache.enabled", this::isAotCacheEnabled)
.description("Whether AOT cache is enabled and loaded")
.register(registry);
Gauge.builder("aot.cache.hit.ratio", this::getCacheHitRatio)
.description("Percentage of methods loaded from cache")
.register(registry);
}
private double isAotCacheEnabled() {
String aotCache = System.getProperty("XX:AOTCache");
String aotMode = System.getProperty("XX:AOTMode");
return (aotCache != null && "load".equals(aotMode)) ? 1.0 : 0.0;
}
private double getCacheHitRatio() {
// Access JVM internals via JMX or internal APIs
// This is illustrative - actual implementation depends on JVM exposure
return 0.85; // Placeholder
}
}
6. Caveats and Limitations
6.1 Cache Invalidation Challenges
The AOT cache contains compiled code and metadata that depends on:
Class file checksums: If any class file changes, the corresponding cache entries are invalid. Even minor code changes invalidate cached compilation results.
JVM version: Cache files are not portable across Java versions. A cache generated with Java 25.0.1 cannot be used with 25.0.2 if internal JVM structures changed.
JVM configuration: Heap sizes, GC algorithms, and other flags affect compilation decisions. The cache must match the production configuration.
Dependency versions: Changes to any dependency class files invalidate portions of the cache, potentially requiring full regeneration.
This means:
Every application version needs a new AOT cache
Caches should be generated in CI/CD, not manually
Cache generation must match production JVM flags exactly
6.2 Training Data Quality
The AOT cache is only as good as the training workload. Poor training leads to:
Incomplete coverage: Methods not executed during training remain uncached. First execution still pays JIT compilation cost.
Suboptimal optimizations: If training load doesn’t match production patterns, the compiler may make wrong inlining or optimization decisions.
Biased compilation: Over-representing rare code paths in training can waste cache space and lead to suboptimal production performance.
Best practices for training:
Execute all critical business operations
Include authentication and authorization paths
Trigger database queries and external API calls
Exercise error handling paths
Match production request distribution as closely as possible
6.3 Memory Overhead
The AOT cache file is memory mapped and consumes address space:
Small applications: 20-50MB cache file Medium applications: 50-150MB cache file Large applications: 150-400MB cache file
This is additional overhead beyond normal heap requirements. For memory constrained environments, the tradeoff may not be worthwhile. Calculate whether startup time savings justify the persistent memory consumption.
6.4 Build Time Implications
Generating AOT caches adds time to the build process:
Typical overhead: 60-180 seconds per build Components:
Application startup for training: 20-60s
Training workload execution: 30-90s
Cache serialization: 10-30s
For large monoliths, this can extend to 5-10 minutes. In CI/CD pipelines with frequent builds, this overhead accumulates. Consider:
Generating caches only for release builds
Caching AOT cache files between similar builds
Parallel cache generation for microservices
6.5 Debugging Complications
Pre-compiled code complicates debugging:
Stack traces: May reference optimized code that doesn’t match source line numbers exactly Breakpoints: Can be unreliable in heavily optimized cached methods Variable inspection: Compiler optimizations may eliminate intermediate variables
For development, disable AOT caching:
# Development environment
java -XX:AOTMode=off -jar myapp.jar
# Or simply omit the AOT flags entirely
java -jar myapp.jar
6.6 Dynamic Class Loading
Applications that generate classes at runtime face challenges:
Dynamic proxies: Generated proxy classes cannot be pre-cached Bytecode generation: Libraries like ASM that generate code at runtime bypass the cache Plugin architectures: Dynamically loaded plugins don’t benefit from main application cache
While the AOT cache handles core application classes well, highly dynamic frameworks may see reduced benefits. Spring’s use of CGLIB proxies and dynamic features means some runtime generation is unavoidable.
6.7 Profile Guided Optimization Drift
Over time, production workload patterns may diverge from training workload:
New features: Added endpoints not in training data Changed patterns: User behavior shifts rendering training data obsolete Seasonal variations: Holiday traffic patterns differ from normal training scenarios
Mitigation strategies:
Regenerate caches with each deployment
Update training workloads based on production telemetry
Monitor cache hit rates and retrain if they degrade
Consider multiple training scenarios for different deployment contexts
1. Load spike detected at t=0
2. HPA triggers scale out at t=10s
3. New pod scheduled at t=15s
4. Container starts at t=20s
5. JVM starts, application initializes at t=32s
6. Pod marked ready, receives traffic at t=35s
Total response time: 35 seconds
With AOT cache:
1. Load spike detected at t=0
2. HPA triggers scale out at t=10s
3. New pod scheduled at t=15s
4. Container starts at t=20s
5. JVM starts with cached data at t=23s
6. Pod marked ready, receives traffic at t=25s
Total response time: 25 seconds (29% improvement)
The 10 second improvement means the system can handle load spikes more effectively before performance degrades.
7.2 Readiness Probe Configuration
Optimize readiness probes for AOT cached applications:
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp-aot
spec:
template:
spec:
containers:
- name: app
readinessProbe:
httpGet:
path: /actuator/health/readiness
port: 8080
# Reduced delays due to faster startup
initialDelaySeconds: 5 # vs 15 for standard JVM
periodSeconds: 2
failureThreshold: 3
livenessProbe:
httpGet:
path: /actuator/health/liveness
port: 8080
initialDelaySeconds: 10 # vs 30 for standard JVM
periodSeconds: 10
This allows Kubernetes to detect and route to new pods much faster, reducing the window of degraded service during scaling events.
# Build with cache
docker build -t myapp:aot .
# Measure startup improvement
time docker run myapp:aot
# Verify functional correctness
./integration-tests.sh
Step 6: Monitor in Production
// Add custom metrics
@Component
public class StartupMetrics implements ApplicationListener<ApplicationReadyEvent> {
@Override
public void onApplicationEvent(ApplicationReadyEvent event) {
long startupTime = System.currentTimeMillis() - event.getTimestamp();
metricsRegistry.gauge("app.startup.duration", startupTime);
}
}
10. Conclusion and Future Outlook
Java 25’s AOT cache represents a pragmatic middle ground between traditional JVM startup characteristics and the extreme optimizations of native compilation. For enterprise Spring applications, the 60-75% startup time improvement comes with minimal code changes and full compatibility with existing frameworks and libraries.
Cost sensitive environments where resource efficiency matters
Applications that cannot adopt GraalVM native image due to dynamic requirements
As the Java ecosystem continues to evolve, AOT caching will likely become a standard optimization technique, much like how JIT compilation became ubiquitous. The relatively simple implementation path and significant performance gains make it accessible to most development teams.
Future enhancements to watch for include:
Improved cache portability across minor Java versions
Automatic training workload generation
Cloud provider managed cache distribution
Integration with service mesh for distributed cache management
For Spring developers specifically, the combination of Spring Boot 3.x native hints, AOT processing, and Java 25 cache support creates a powerful optimization stack that maintains the flexibility of the JVM while approaching native image performance for startup characteristics.
The path forward is clear: as containerization and cloud native architectures become universal, startup time optimization transitions from a nice to have feature to a fundamental requirement. Java 25’s AOT cache provides production ready capability that delivers on this requirement without the complexity overhead of alternative approaches.
If you’re like me, the idea of doing anything twice will make you break out in a cold shiver. For my Claude desktop, I often need network pcap (packet capture) to unpack something that I am doing. So the script below installs wireshark, and then the wireshark mcp and then configures Claude to use it. Then I got it to work with zscaler (note, I just did a process grep – you could also check utun/port 9000/9400).
I also added example scripts to test its working and so prompts to help you test in Claude.
cat > ~/setup_wiremcp_simple.sh << 'EOF'
#!/bin/bash
# Simplified WireMCP Setup with Zscaler Support
echo ""
echo "============================================"
echo " WireMCP Setup with Zscaler Support"
echo "============================================"
echo ""
# Detect Zscaler
echo "[INFO] Detecting Zscaler..."
ZSCALER_DETECTED=false
ZSCALER_INTERFACE=""
# Check for Zscaler process
if pgrep -f "Zscaler" >/dev/null 2>&1; then
ZSCALER_DETECTED=true
echo "[ZSCALER] ✓ Zscaler process is running"
fi
# Find Zscaler tunnel interface
UTUN_INTERFACES=$(ifconfig -l | grep -o 'utun[0-9]*')
for iface in $UTUN_INTERFACES; do
IP=$(ifconfig "$iface" 2>/dev/null | grep "inet " | awk '{print $2}')
if [[ "$IP" == 100.64.* ]]; then
ZSCALER_INTERFACE="$iface"
ZSCALER_DETECTED=true
echo "[ZSCALER] ✓ Zscaler tunnel found: $iface (IP: $IP)"
break
fi
done
if [[ "$ZSCALER_DETECTED" == "true" ]]; then
echo "[ZSCALER] ✓ Zscaler environment confirmed"
else
echo "[INFO] No Zscaler detected - standard network"
fi
echo ""
# Check existing installations
echo "[INFO] Checking installed software..."
if command -v tshark >/dev/null 2>&1; then
echo "[✓] Wireshark/tshark is installed"
else
echo "[!] Wireshark not found - install with: brew install --cask wireshark"
fi
if command -v node >/dev/null 2>&1; then
echo "[✓] Node.js is installed: $(node --version)"
else
echo "[!] Node.js not found - install with: brew install node"
fi
if [[ -d "$HOME/WireMCP" ]]; then
echo "[✓] WireMCP is installed at ~/WireMCP"
else
echo "[!] WireMCP not found"
fi
echo ""
# Configure SSL decryption for Zscaler
if [[ "$ZSCALER_DETECTED" == "true" ]]; then
echo "[INFO] Configuring SSL/TLS decryption..."
SSL_KEYLOG="$HOME/.wireshark-sslkeys.log"
touch "$SSL_KEYLOG"
chmod 600 "$SSL_KEYLOG"
if ! grep -q "SSLKEYLOGFILE" ~/.zshrc 2>/dev/null; then
echo "" >> ~/.zshrc
echo "# Wireshark SSL/TLS decryption for Zscaler" >> ~/.zshrc
echo "export SSLKEYLOGFILE=\"$SSL_KEYLOG\"" >> ~/.zshrc
echo "[✓] Added SSLKEYLOGFILE to ~/.zshrc"
else
echo "[✓] SSLKEYLOGFILE already in ~/.zshrc"
fi
echo "[✓] SSL key log file: $SSL_KEYLOG"
fi
echo ""
# Update WireMCP for Zscaler
if [[ -d "$HOME/WireMCP" ]]; then
if [[ "$ZSCALER_DETECTED" == "true" ]]; then
echo "[INFO] Creating Zscaler-aware wrapper..."
cat > "$HOME/WireMCP/start_zscaler.sh" << 'WRAPPER'
#!/bin/bash
echo "=== WireMCP (Zscaler Mode) ==="
# Set SSL decryption
export SSLKEYLOGFILE="$HOME/.wireshark-sslkeys.log"
# Find Zscaler interface
UTUN_LIST=$(ifconfig -l | grep -o 'utun[0-9]*')
for iface in $UTUN_LIST; do
IP=$(ifconfig "$iface" 2>/dev/null | grep "inet " | awk '{print $2}')
if [[ "$IP" == 100.64.* ]]; then
export CAPTURE_INTERFACE="$iface"
echo "✓ Zscaler tunnel: $iface ($IP)"
echo "✓ All proxied traffic flows through this interface"
break
fi
done
if [[ -z "$CAPTURE_INTERFACE" ]]; then
export CAPTURE_INTERFACE="en0"
echo "! Using default interface: en0"
fi
echo ""
echo "Configuration:"
echo " SSL Key Log: $SSLKEYLOGFILE"
echo " Capture Interface: $CAPTURE_INTERFACE"
echo ""
echo "To capture: sudo tshark -i $CAPTURE_INTERFACE -c 10"
echo "===============================\n"
cd "$(dirname "$0")"
node index.js
WRAPPER
chmod +x "$HOME/WireMCP/start_zscaler.sh"
echo "[✓] Created ~/WireMCP/start_zscaler.sh"
fi
# Create test script
cat > "$HOME/WireMCP/test_zscaler.sh" << 'TEST'
#!/bin/bash
echo "=== Zscaler & WireMCP Test ==="
echo ""
# Check Zscaler process
if pgrep -f "Zscaler" >/dev/null; then
echo "✓ Zscaler is running"
else
echo "✗ Zscaler not running"
fi
# Find tunnel
UTUN_LIST=$(ifconfig -l | grep -o 'utun[0-9]*')
for iface in $UTUN_LIST; do
IP=$(ifconfig "$iface" 2>/dev/null | grep "inet " | awk '{print $2}')
if [[ "$IP" == 100.64.* ]]; then
echo "✓ Zscaler tunnel: $iface ($IP)"
FOUND=true
break
fi
done
[[ "$FOUND" != "true" ]] && echo "✗ No Zscaler tunnel found"
echo ""
# Check SSL keylog
if [[ -f "$HOME/.wireshark-sslkeys.log" ]]; then
SIZE=$(wc -c < "$HOME/.wireshark-sslkeys.log")
echo "✓ SSL key log exists ($SIZE bytes)"
else
echo "✗ SSL key log not found"
fi
echo ""
echo "Network interfaces:"
tshark -D 2>/dev/null | head -5
echo ""
echo "To capture Zscaler traffic:"
echo " sudo tshark -i ${iface:-en0} -c 10"
TEST
chmod +x "$HOME/WireMCP/test_zscaler.sh"
echo "[✓] Created ~/WireMCP/test_zscaler.sh"
fi
echo ""
# Configure Claude Desktop
CLAUDE_CONFIG="$HOME/Library/Application Support/Claude/claude_desktop_config.json"
if [[ -d "$(dirname "$CLAUDE_CONFIG")" ]]; then
echo "[INFO] Configuring Claude Desktop..."
# Backup existing
if [[ -f "$CLAUDE_CONFIG" ]]; then
BACKUP_FILE="${CLAUDE_CONFIG}.backup.$(date +%Y%m%d_%H%M%S)"
cp "$CLAUDE_CONFIG" "$BACKUP_FILE"
echo "[✓] Backup created: $BACKUP_FILE"
fi
# Check if jq is installed
if ! command -v jq >/dev/null 2>&1; then
echo "[INFO] Installing jq for JSON manipulation..."
brew install jq
fi
# Create temp capture directory
TEMP_CAPTURE_DIR="$HOME/.wiremcp/captures"
mkdir -p "$TEMP_CAPTURE_DIR"
echo "[✓] Capture directory: $TEMP_CAPTURE_DIR"
# Prepare environment variables
if [[ "$ZSCALER_DETECTED" == "true" ]]; then
ENV_JSON=$(jq -n \
--arg ssllog "$HOME/.wireshark-sslkeys.log" \
--arg iface "${ZSCALER_INTERFACE:-en0}" \
--arg capdir "$TEMP_CAPTURE_DIR" \
'{"SSLKEYLOGFILE": $ssllog, "CAPTURE_INTERFACE": $iface, "ZSCALER_MODE": "true", "CAPTURE_DIR": $capdir}')
else
ENV_JSON=$(jq -n \
--arg capdir "$TEMP_CAPTURE_DIR" \
'{"CAPTURE_DIR": $capdir}')
fi
# Add or update wiremcp in config, preserving existing servers
if [[ -f "$CLAUDE_CONFIG" ]] && [[ -s "$CLAUDE_CONFIG" ]]; then
echo "[INFO] Merging WireMCP into existing config..."
jq --arg home "$HOME" \
--argjson env "$ENV_JSON" \
'.mcpServers.wiremcp = {"command": "node", "args": [$home + "/WireMCP/index.js"], "env": $env}' \
"$CLAUDE_CONFIG" > "${CLAUDE_CONFIG}.tmp" && mv "${CLAUDE_CONFIG}.tmp" "$CLAUDE_CONFIG"
else
echo "[INFO] Creating new Claude config..."
jq -n --arg home "$HOME" \
--argjson env "$ENV_JSON" \
'{"mcpServers": {"wiremcp": {"command": "node", "args": [$home + "/WireMCP/index.js"], "env": $env}}}' \
> "$CLAUDE_CONFIG"
fi
if [[ "$ZSCALER_DETECTED" == "true" ]]; then
echo "[✓] Claude configured with Zscaler mode"
else
echo "[✓] Claude configured"
fi
echo "[✓] Existing MCP servers preserved"
fi
echo ""
echo "============================================"
echo " Summary"
echo "============================================"
echo ""
if [[ "$ZSCALER_DETECTED" == "true" ]]; then
echo "Zscaler Environment:"
echo " ✓ Detected and configured"
[[ -n "$ZSCALER_INTERFACE" ]] && echo " ✓ Tunnel interface: $ZSCALER_INTERFACE"
echo " ✓ SSL decryption ready"
echo ""
echo "Next steps:"
echo " 1. Restart terminal: source ~/.zshrc"
echo " 2. Restart browsers for HTTPS decryption"
else
echo "Standard Network:"
echo " • No Zscaler detected"
echo " • Standard configuration applied"
fi
echo ""
echo "For Claude Desktop:"
echo " 1. Restart Claude Desktop app"
echo " 2. Ask Claude to analyze network traffic"
echo ""
echo "============================================"
exit 0
EOF
chmod +x ~/setup_wiremcp_simple.sh
To test if the script worked:
cat > ~/test_wiremcp_claude.sh << 'EOF'
#!/bin/bash
# WireMCP Claude Desktop Interactive Test Script
echo "╔════════════════════════════════════════════════════════╗"
echo "║ WireMCP + Claude Desktop Testing Tool ║"
echo "╚════════════════════════════════════════════════════════╝"
echo ""
# Colors
GREEN='\033[0;32m'
BLUE='\033[0;34m'
YELLOW='\033[1;33m'
NC='\033[0m'
# Check prerequisites
echo -e "${BLUE}[1/4]${NC} Checking prerequisites..."
if ! command -v tshark >/dev/null 2>&1; then
echo " ✗ tshark not found"
exit 1
fi
if [[ ! -d "$HOME/WireMCP" ]]; then
echo " ✗ WireMCP not found at ~/WireMCP"
exit 1
fi
if [[ ! -f "$HOME/Library/Application Support/Claude/claude_desktop_config.json" ]]; then
echo " ⚠ Claude Desktop config not found"
fi
echo -e " ${GREEN}✓${NC} All prerequisites met"
echo ""
# Detect Zscaler
echo -e "${BLUE}[2/4]${NC} Detecting network configuration..."
ZSCALER_IF=""
for iface in $(ifconfig -l | grep -o 'utun[0-9]*'); do
IP=$(ifconfig "$iface" 2>/dev/null | grep "inet " | awk '{print $2}')
if [[ "$IP" == 100.64.* ]]; then
ZSCALER_IF="$iface"
echo -e " ${GREEN}✓${NC} Zscaler tunnel: $iface ($IP)"
break
fi
done
if [[ -z "$ZSCALER_IF" ]]; then
echo " ⚠ No Zscaler tunnel detected (will use en0)"
ZSCALER_IF="en0"
fi
echo ""
# Generate test traffic
echo -e "${BLUE}[3/4]${NC} Generating test network traffic..."
# Background network requests
(curl -s https://api.github.com/zen > /dev/null 2>&1) &
(curl -s https://httpbin.org/get > /dev/null 2>&1) &
(curl -s https://www.google.com > /dev/null 2>&1) &
(ping -c 3 8.8.8.8 > /dev/null 2>&1) &
sleep 2
echo -e " ${GREEN}✓${NC} Test traffic generated (GitHub, httpbin, Google, DNS)"
echo ""
# Show test prompts
echo -e "${BLUE}[4/4]${NC} Test prompts for Claude Desktop"
echo "════════════════════════════════════════════════════════"
echo ""
echo -e "${YELLOW}📋 Copy these prompts into Claude Desktop:${NC}"
echo ""
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "TEST 1: Basic Connection Test"
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
cat << 'EOF'
Can you see the WireMCP tools? List all available network analysis capabilities you have access to.
EOF
echo ""
echo "Expected: Claude should list 7 tools (capture_packets, get_summary_stats, etc.)"
echo ""
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "TEST 2: Simple Packet Capture"
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
cat << 'EOF'
Capture 20 network packets and show me a summary including:
- Source and destination IPs
- Protocols used
- Port numbers
- Any interesting patterns
EOF
echo ""
echo "Expected: Packets from $ZSCALER_IF with IPs in 100.64.x.x range"
echo ""
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "TEST 3: Protocol Analysis"
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
cat << 'EOF'
Capture 50 packets and show me:
1. Protocol breakdown (TCP, UDP, DNS, HTTP, TLS)
2. Which protocol is most common
3. Protocol hierarchy statistics
EOF
echo ""
echo "Expected: Protocol percentages and hierarchy tree"
echo ""
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "TEST 4: Connection Analysis"
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
cat << 'EOF'
Capture 100 packets and show me network conversations:
- Top 5 source/destination pairs
- Number of packets per conversation
- Bytes transferred
EOF
echo ""
echo "Expected: Conversation statistics with packet/byte counts"
echo ""
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "TEST 5: Threat Detection"
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
cat << 'EOF'
Capture traffic for 30 seconds and check all destination IPs against threat databases. Tell me if any malicious IPs are detected.
EOF
echo ""
echo "Expected: List of IPs and threat check results (should show 'No threats')"
echo ""
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "TEST 6: HTTPS Decryption (Advanced)"
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
echo "⚠️ First: Restart your browser after running this:"
echo " source ~/.zshrc && echo \$SSLKEYLOGFILE"
echo ""
cat << 'EOF'
Capture 30 packets while I browse some HTTPS websites. Can you see any HTTP hostnames or request URIs from the HTTPS traffic?
EOF
echo ""
echo "Expected: If SSL keys are logged, Claude sees decrypted HTTP data"
echo ""
echo "════════════════════════════════════════════════════════"
echo ""
echo -e "${YELLOW}🔧 Manual Verification Commands:${NC}"
echo ""
echo " # Test manual capture:"
echo " sudo tshark -i $ZSCALER_IF -c 10"
echo ""
echo " # Check SSL keylog:"
echo " ls -lh ~/.wireshark-sslkeys.log"
echo ""
echo " # Test WireMCP server:"
echo " cd ~/WireMCP && timeout 3 node index.js"
echo ""
echo " # Check Claude config:"
echo " cat \"\$HOME/Library/Application Support/Claude/claude_desktop_config.json\""
echo ""
echo "════════════════════════════════════════════════════════"
echo ""
echo -e "${GREEN}✅ Test setup complete!${NC}"
echo ""
echo "Next steps:"
echo " 1. Open Claude Desktop"
echo " 2. Copy/paste the test prompts above"
echo " 3. Verify Claude can access WireMCP tools"
echo " 4. Check ~/WIREMCP_TESTING_EXAMPLES.md for more examples"
echo ""
# Keep generating traffic in background
echo "Keeping test traffic active for 2 minutes..."
echo "(You can Ctrl+C to stop)"
echo ""
# Generate continuous light traffic
for i in {1..24}; do
(curl -s https://httpbin.org/delay/1 > /dev/null 2>&1) &
sleep 5
done
echo ""
echo "Traffic generation complete!"
echo ""
EOF
chmod +x ~/test_wiremcp_claude.sh
Now that you have tested everything is fine… the below just gives you a few example tests to carry out.
# Try WireMCP Right Now! 🚀
## 🎯 3-Minute Quick Start
### Step 1: Restart Claude Desktop (30 seconds)
```bash
# Kill and restart Claude
killall Claude
sleep 2
open -a Claude
```
### Step 2: Create a script to Generate Some Traffic (30 seconds)
cat > ~/network_activity_loop.sh << 'EOF'
#!/bin/bash
# Script to generate network activity for 30 seconds
# Useful for testing network capture tools
echo "Starting network activity generation for 30 seconds..."
echo "Press Ctrl+C to stop early if needed"
# Record start time
start_time=$(date +%s)
end_time=$((start_time + 30))
# Counter for requests
request_count=0
# Loop for 30 seconds
while [ $(date +%s) -lt $end_time ]; do
# Create network activity to capture
echo -n "Request set #$((++request_count)) at $(date +%T): "
# GitHub API call
curl -s https://api.github.com/users/octocat > /dev/null 2>&1 &
# HTTPBin JSON endpoint
curl -s https://httpbin.org/json > /dev/null 2>&1 &
# IP address check
curl -s https://ifconfig.me > /dev/null 2>&1 &
# Wait for background jobs to complete
wait
echo "completed"
# Small delay to avoid overwhelming the servers
sleep 0.5
done
echo ""
echo "Network activity generation completed!"
echo "Total request sets sent: $request_count"
echo "Duration: 30 seconds"
EOF
chmod +x ~/network_activity_loop.sh
# Call the script
./network_activity_loop.sh
Time to play!
Now open Claude Desktop and we can run a few tests…
Ask Claude:
Can you see the WireMCP tools? List all available network analysis capabilities.
Claude should list 7 tools: – capture_packets – get_summary_stats – get_conversations – check_threats – check_ip_threats – analyze_pcap – extract_credentials
2. Ask Claude:
Capture 20 network packets and tell me: – What IPs am I talking to? – What protocols are being used? – Anything interesting?
I just called api.github.com. Can you capture my network traffic for 10 seconds and tell me: 1. What IP did GitHub resolve to? 2. How long did the connection take? 3. Were there any errors?
4. Ask Claude:
Monitor my network for 30 seconds and show me: – Top 5 destinations by packet count – What services/companies am I connecting to? – Any unexpected connections?
I’m calling myapi.company.com and it feels slow. Capture traffic for 30 seconds while I make a request and tell me: – Where is the latency coming from? – DNS, TCP handshake, TLS, or server response? – Any retransmissions?
I’m getting timeouts to db.example.com:5432. Capture for 30 seconds and tell me: 1. Is DNS resolving? 2. Are SYN packets being sent? 3. Do I get SYN-ACK back? 4. Any firewall blocking?
7. TLS Handshake failures (often happen with zero trust networks and cert pinning). Ask Claude:
Monitor my network for 2 mins and look for abnormal TLS handshakes, in particular shortlived TLS handshakes, which can occur due to cert pinning issues.
8. Check for Threats. Ask Claude:
Monitor my network for 60 seconds and check all destination IPs against threat databases. Tell me if anything suspicious.
9. Monitor Background Apps. Ask Claude:
Capture traffic for 30 seconds while I’m idle. What apps are calling home without me knowing? Only get conversation statistics to show the key connections and the amount of traffic through each. Show any failed traffic or unusual traffic patterns
10. VPN Testing. Ask Claude:
Capture packets for 60 seconds, during which time i will enable my VPN. Compare the difference and see if you can see exactly when my VPN was enabled.
11. Audit traffic. Ask Claude:
Monitor for 5 minutes and tell me: – Which service used most bandwidth? – Any large file transfers? – Unexpected data usage?
12. Looking for specific protocols. Ask Claude:
Monitor my traffic for 30 seconds and see if you can spot any traffic using QUIC and give me statistics on it.
(then go open a youtube website)
13. DNS Queries. Ask Claude:
As a network troubleshooter, analyze all DNS queries for 30 seconds and provide potential causes for any errors. Show me detailed metrics on any calls, especially failed calls or unusual DNS patterns (like NXDOMAIN, PTR or TXT calls)
14. Certificate Issues. Ask Claude:
Capture TLS handshakes for the next minute and show me the certificate chain. Look out for failed/short live TLS sessions
What Makes This Powerful?
The tradition way used to be:
“`bash sudo tcpdump -i utun5 -w capture.pcap # Wait… # Stop capture # Open Wireshark # Apply filters # Analyze packets manually # Figure out what it means “` Time: 10-30 minutes!
With WireMCP + Claude:
“Capture my network traffic and tell me what’s happening in plain English”
Time: 30 seconds
Claude automatically: – Captures on correct interface (utun5) – Filters relevant packets – Analyzes protocols – Identifies issues – Explains in human language – Provides recommendations
